With the rapid growth of Electric Vehicle (EV) technology, EVs are destined to shape the future of transportation. The large number of EVs facilitates the development of the emerging vehicle-to-grid (V2G) technology, which realizes bidirectional energy exchanges between EVs and the power grid. This has led to the setting up of electricity markets that are usually confined to a small geographical location, often with a small number of participants. Usually, these markets are manipulated by intermediaries responsible for collecting bids from prosumers, determining the market-clearing price, incorporating grid constraints, and accounting for network losses. While centralized models can be highly efficient, they grant excessive power to the intermediary by allowing them to gain exclusive access to prosumers \textquotesingle price preferences. This opens the door to potential market manipulation and raises significant privacy concerns for users, such as the location of energy providers. This lack of protection exposes users to potential risks, as untrustworthy servers and malicious adversaries can exploit this information to infer trading activities and real identities. This work proposes a secure, decentralized exchange market built on blockchain technology, utilizing a privacy-preserving Automated Market Maker (AMM) model to offer open and fair, and equal access to traders, and mitigates the most common trading-manipulation attacks. Additionally, it incorporates a scalable architecture based on geographical dynamic sharding, allowing for efficient resource allocation and improved performance as the market grows.
The development of blockchain technology has significantly enhanced the security and transparency of personal information and transaction records. Concurrent with the advancement of blockchain technology and the emergence of the digital currency ecosystem, the internet has evolved from a paradigm dominated by information flow to one driven by value flow. Consequently, the concept of token has gained widespread dissemination, and the electronic token under investigation in this thesis is a development of this concept. The application of electronic tokens has become pervasive with the development of the internet, but the functionality of these tokens is often limited, and issues related to trust remain significant challenges. This study proposes innovative solutions to address the deficiencies in traditional electronic token systems, including the issuance of tokens, the heterogeneity of issuance standards, and the lack of democracy. The solutions are based on distributed storage, anti-tampering mechanisms, consensus protocols, and transparent, traceable storage. Additionally, the study employs NFTs, smart contracts, and RSA blind signatures, among other key technologies, to construct a system based on blockchain technology. The process integrates the decentralised management and centralised operation models, aligning them with the national policy directives. The developed solution enables the full utilisation of blockchain technology's advantages while also fostering community participation. Consequently, it establishes a secure, legal, reliable, and dynamic electronic certification system.
Non-Terrestrial Networks (NTNs) based on Unmanned Aerial Vehicles (UAVs) as base stations are extremely susceptible to security attacks due to their distributed and dynamic nature, which makes them vulnerable to rogue nodes. In this paper, a new Dynamic Trust Score Adjustment Mechanism with Energy-Aware Consensus (DTSAM-EAC) is proposed to enhance security in UAV-based NTNs. The proposed framework integrates a permissioned Hyperledger Fabric blockchain with Federated Learning (FL) to support privacy-preserving trust evaluation. Trust ratings are updated continuously through weighted aggregation of past trust, present behavior, and energy contribution, thus making the system adaptive to changing network conditions. An energy-aware consensus mechanism prioritizes UAVs with greater available energy for block validation, ensuring efficient use of resources under resource-constrained environments. FL aggregation with trust-weighting further increases the resilience of the global trust model. Simulation results verify the designed framework achieves 94\% trust score prediction accuracy and 96\% rogue UAV detection rate while outperforming centralized and static baselines of trust-based solutions on privacy, energy efficiency, and reliability. It complies with 6G requirements in terms of distributed intelligence and sustainability and is an energy-efficient and scalable solution to secure NTNs.
Collins Izuchukwu Okafor, Love Allen Chijioke Ahakonye, Jae Min Lee, Dong‐Seong Kim
Integrating Internet of Things (IoT) networks with blockchain technology has introduced transformative potential for secure, decentralized applications. However, with expanding IoT networks and the rapid advancements in quantum computing, which can break the classical encryption mechanism employed in these networks, securing validator communications in blockchain consensus mechanisms becomes increasingly challenging. This study presents PureQuantum, a novel quantum-enhanced framework that integrates quantum key distribution (QKD) into a proof-of-authority and association (PoA 2 ) consensus mechanism. Our experimental evaluation demonstrates that PureQuantum achieves a key generation time of approximately 2.315 s (BB84) and an average error rate of 0.2493, thereby ensuring robust resistance to classical and quantum computational threats. Exploring the architecture, design, and performance of PureQuantum, this concept highlights its ability to balance scalability, energy efficiency, and quantum-resilient security. By integrating QKD into validator communication protocols, PureQuantum provides a future-proof framework capable of supporting the demands of IoT-enabled decentralized systems.
This research examines the application of Long Short-Term Memory (LSTM) neural networks for predicting cryptocurrency prices, with a focus on Bitcoin (BTC) and Ethereum (ETH), the two dominant digital assets with the highest market capitalization. The study addresses the critical challenge of accurately forecasting cryptocurrency price movements in highly volatile markets, which is essential for informed investment decision-making in the digital economy. The methodology employs LSTM models trained on historical closing price data from 2014 to 2024 for Bitcoin and from 2017 to 2024 for Ethereum, utilizing an 80:20 training-to-testing ratio. Results demonstrate exceptional predictive accuracy with R² values of 99.08% for Bitcoin and 97.44% for Ethereum, while MAPE values remained low at 1.8% and 1.9%, respectively. The study concludes that LSTM models effectively capture complex patterns in cryptocurrency price movements, providing reliable short-term forecasting capabilities and contributing valuable insights to the intersection of artificial intelligence and digital economy development.
This study aims to analyze the contribution of village funds, Islamic financial institutions, and regional economic integration to regional economic growth. In the context of fiscal decentralization and regional development, these three variables play a strategic role in accelerating inclusive and sustainable economic development. Village funds serve as the primary instrument to finance infrastructure development and community empowerment. Meanwhile, Islamic financial institutions provide access to fair and Sharia-compliant financing, supporting the productivity of micro and small enterprises. Regional economic integration, particularly through inter-regional connectivity and cooperation, strengthens local markets and enhances regional competitiveness. The analysis method uses a descriptive-qualitative approach based on literature studies and secondary data. The findings indicate that the synergy among the three variables can drive equitable economic growth, although integrated policies and institutional capacity strengthening are still needed.
Quantum pseudorandomness is an emerging research area. Ji, Liu, and Song defined pseudorandom states (PRSs) and pseudorandom unitaries (PRUs) as quantum analogs of pseudorandom generators and pseudorandom functions. A unitary oracle separation result between one-way functions and PRSs/PRUs, established by Kretschmer, suggests that certain quantum primitives may remain secure even if classical cryptography is compromised. This insight has spurred extensive work on quantum pseudorandomness and its applications in quantum cryptography. Many constructions of PRSs have been established under standard assumptions, yet building a secure PRU was a long-standing open problem. This dissertation aims to narrow the gap between PRSs and PRUs and presents results that go beyond PRSs. We introduce Pseudorandom State Scramblers (PRSSs), a new primitive that lies between PRSs and PRUs. A PRSS maps any pure state to a pseudorandom state, a property shared with PRUs but not with PRSs. We present a construction of PRSSs inspired by the well-known Kac’s walk, and in particular, we develop a parallel variant that significantly accelerates the mixing time, enabling an efficient construction. PRSSs support cryptographic tasks not known to be achievable from PRSs alone, including a quantum encryption scheme and a succinct quantum state commitment. Additionally, when suitable classical randomness is provided, our construction exhibits a special dispersing property not known to be satisfied by any existing construction of quantum pseudorandom primitives. Our subsequent work shows that, without asymptotically increasing the number of steps, our construction based on the parallel Kac’s walk yields PRUs with standard or even strong security. The proof builds on a recently developed technique for establishing adaptive security, known as the path-recording method. This result provides an alternative construction of PRUs and further showcases the power of this proof technique. In addition, this dissertation includes two side projects. The first revisits the Hidden Subgroup Problem over ℤn, providing a simplified analysis of a known quantum algorithm using elementary lattice tools. The second establishes a quantum analogue of a classical impossibility result for statistical non-interactive zero-knowledge arguments, showing limitations of black-box reductions under classical-query quantum adversaries.
The foundations of successful European Union policies and current initiatives on the adaptation of the energy sector to climate change, aimed at transforming Europe into a climate-neutral continent by 2050, are considered. A comprehensive analytical approach is proposed, consisting of regulatory, political and institutional analysis and elements of content analysis of EU strategic documents in the field of climate and energy, in particular the European Green Deal (2019), the EU Climate Law (2021), the "Fit for 55" Package (2021), the RED II / RED III Directive, the Energy Efficiency Directives (EED). Analysis shows that to achieve climate neutrality in the EU, a reduction of greenhouse gas emissions by 55 % by 2030 (compared to the 1990 level) is envisaged; increasing the share of renewable energy sources − up to 42.5 % by 2030; increasing energy efficiency − reducing total energy consumption by 11.7 % by 2030. The EU has developed the main policy directions for adapting the energy sector to climate change, in particular: integrating adaptation into energy policy (planning) at all levels; development of sustainable energy infrastructure (modernization of networks, decentralization of energy, investment in "smart grids"); development of renewable energy sources; financing and support for research; cooperation at the national and regional levels; monitoring and vulnerability assessment. Analysis of EU legislation in the fields of climate and energy indicates the functioning of a complex system of interconnected regulatory acts, which shapes European energy policy within the framework of the European Green Deal. This is what should become the basis for Ukraine's formation of its green deal, which has recently initiated. Keywords: energy sector, climate change, risk, adaptation, public policy, European Union.
Psychedelic Minimalism: The Case Against Music in Psychedelic Therapy SettingsIn response to: Kaelen et al. (2018), “The Hidden Therapist: Evidence for a Central Role ofMusic in Psychedelic Therapy.”Introduction: Reframing the Role of SettingIn recent years, renewed attention has been given to the centrality of set and setting inpsychedelic-assisted therapy. The Reporting of Setting in Psychedelic Clinical Trials (ReSPCT)guidelines reflect an evolving consensus: therapeutic outcomes are not solely pharmacologicallydetermined but are heavily shaped by both the environment and the psychological state of theparticipant (Pronovost-Morgan et al. 2025). Within this evolving paradigm, music has assumed aprivileged role, framed as a therapeutic constant, typically curated to be emotionally supportiveor evocative, and especially emphasized during peak psychedelic states (Barrett et al. 2017).The prevailing assumption is that music facilitates safety, emotional access, and therapeuticalignment (Kaelen et al. 2018). However, this assumption has not been subjected to the samedegree of empirical or philosophical scrutiny that other elements of the setting have receivedIn this opinion, I offer a contrarian view: that the inclusion of music in psychedelic therapy maynot be benign or beneficial but could instead function as a confounding variable, altering,distorting, or displacing the very psychological material that therapy seeks to access. I arguethat music introduces external emotional content that may obscure the patient’s naturalpsychological flow, their set, thereby undermining the core objective of psychedelic therapy:unmediated engagement with the self. This perspective, which I call psychedelic minimalism,challenges the default assumption that additive features of the setting, particularly music, areinherently therapeutic. Rather than continuing to ask what kind of music, or how it’s played, bestsupports the psychedelic experience, I suggest we ask a more foundational question: whymusic at all? If the therapeutic goal is to access internal, unfiltered psychological material, thenany emotionally potent stimulus, no matter how well-curated, may interfere with that process. Ialso call for further research to fill this gap, exploring whether a minimalist setting, free ofemotional modulation, offers deeper, more authentic access to the self.Setting, Set, and the Risk of Emotional InterferenceIn psychedelic therapy, set refers to the totality of a patient’s internal state, their beliefs,thoughts, personality, and emotional tendencies, while setting denotes the external environmentwith its sensory, and interpersonal dimensions (Eisner 1997). Together, set and setting shapethe content, tone, and trajectory of the psychedelic experience (Hartogsohn 2016).Psychedelics generate non-ordinary states of consciousness (NOSCs) not inherentlytherapeutic in themselves but filled with potential emerging from lowered psychologicaldefenses, allowing dissociated material to surface (Grof 2000). When this material is engagedwith, the therapeutic action of psychedelics obtains; the role of the setting should not be to guidethe patient away from this material, but to allow its unfolding with as little interference aspossible allowing for direct engagement.To that end, the ideal therapeutic setting is emotionally neutral, a vessel that supports theemergence of unfiltered psychological content without adding its own emotional coloration. Iargue that psychedelic therapeutic efficacy hinges most critically on preserving the primacy ofthe set, enabling the individual to encounter their psyche without distortion or displacement fromexternal stimuli; namely, that the goal of what I refer to as the pure set encounter is a settingdesigned to unconfound the set, where every additive feature of the setting is evaluated for howit alters the natural flow of cognition and introspection.Music as a Modulator of Emotion and MeaningAmong all components of the therapeutic setting, music stands out as a uniquely potentpsychological force. It is not ambient noise or passive background, rather, it is emotionallyloaded, temporally structured, and rich with meaning (Bonny and Pahnke 1972). Music inpsychedelic therapy is often justified for its ability to guide, ground, or emotionally stabilize thepatient during challenging experiences (Kaelen et al. 2018). Yet it is this very power thatwarrants caution.I propose that music operates as a phenomenological modulating influence, shaping thepatient’s emotional and cognitive flow during psychedelic sessions. Clinical data and emergingtheories in affective neuroscience and music therapy suggest two primary axes by which musicmay exert its effects: emotional provocativeness, or the ability to evoke strong affective stateslike awe, nostalgia, grief, or anxiety, and personal meaning, referring to the autobiographicalassociations embedded within particular songs (Barrett, Preller, et al. 2017; Barrett, Preller, andKaelen 2018; Strickland, Garcia-Romeu, and Johnson 2020). Music scoring high on eitherdimension, let alone both, I argue, risks overpowering the patient’s natural stream of thoughtand redirecting attention to emotionally charged content that may not have otherwise emergedThe influence of provocative music has been observed clinically and acknowledged even in theReSPCT Guidelines (Item 11), which call for careful reporting of the music used, given itsevident impact on the therapeutic setting (“The ReSPCT Guidelines” 2025). One case studyparticipant described her psilocybin session as uncomfortable when accompanied by music, butcalming and insightful during silent periods that involved therapist-guided mindfulness andinterpersonal discussion. She ultimately chose to move to a quiet room, preferring a music-freesetting (Gloeckler et al. 2024). Another participant stated that “under psilocybin I felt almost thatI had no choice but to go with the music” (Kaelen et al. 2018), implying a surrender of agency tothe external structure of sound. These accounts suggest that music, far from universallysupportive, can inhibit introspection and redirect the therapeutic process away from self-generated insight.Proponents of music often argue that it enhances the psychedelic experience and supportspatients through difficult terrain (Kaelen et al. 2015). But this assumption is worth challenging. Inmany cases, meaningful breakthroughs occur not through comfort but through productivediscomfort: the slow processing of unstructured inner chaos, the confrontation with long-avoidedemotions, or the resurfacing of deeply repressed memory. One psilocybin trial participantdescribed her musical experience as overwhelming, reporting that she felt she was“suffocating,” and that the music was “too much” (Gloeckler et al. 2024). Only during a music-free period did she experience release from resistance and begin productive introspection.Even music that holds personal significance may be counterproductive. A song tied to thememory of a lost loved one may elicit genuine sadness, but that sadness is structured by themusic’s associative framework, not by the patient’s internal emotional terrain. While some mayargue that such triggers can facilitate emotional processing, especially when linked to trauma,the ability to identify and select such music in advance is clinically uncertain and not scalable.Many patients are unaware of how particular songs will affect them until they are in the alteredstate. Furthermore, music that appears helpful in one moment may overwhelm or displace morerelevant material that would otherwise emerge in silence. The risk here is not just distraction,but displacement: the substitution of an externally prompted emotional script for an internallydriven exploration.Gaps in Research, Future Directions, and Analysis of Conflicts of InterestDespite the central role music plays in most psychedelic-assisted therapy protocols, there is astriking lack of controlled research examining its specific effects. Music is often treated as adefault or essential component, not as a testable variable. This has left a significant gap in ourunderstanding of how therapeutic outcomes differ between music-enriched and minimalist(silent) settings. The assumption of music’s benefit remains largely unchallenged, limiting insightinto which aspects of the psychedelic experience are truly intrinsic and which are shaped byexternal influences.This oversight presents a valuable opportunity for future research. Clinically testable questions,such as whether music fosters emotional insight or deflects it, and whether insights gained insilence are more enduring, could inform more effective, scalable treatment models. Randomizedtrials, qualitative and neurophenomenological studies, and long-term follow-ups comparingdifferent settings could clarify whether music enhances or interferes with therapeutic goals.Rather than accepting tradition, researchers must begin to treat music as an active variablewhose inclusion requires empirical justification.It is also not without mention, notable to consider the potential conflict between author MendelKaelen, PhD, the first author of the dissented paper, and his role as CEO of Wavepaths, a“startup providing music both for and as psychedelic therapy” (ICPR by OPEN Foundation 2024;Wavepaths 2024). If Dr. Kaelen has a financial stake in the use of music in the setting ofpsychedelic assisted therapy, this invites scrutiny of potential influence over confirmation bias inhis research, especially given the nascent, subjective nature of current psychedelic medicineresearch.DiscussionPsychedelic therapy is most powerful when it enables unmediated access to the self. Yet music,widely assumed to be therapeutic, may disrupt this process by directing attention and shapingemotional response. As a modulating force, it risks overshadowing the very psychologicalmaterial therapy aims to access. Emotional experiences prompted by music may feelmeaningful but are often structured by the music itself, not by the patient’s own psyche. Aminimalist setting that is quiet, neutral, and non-directive, offers an alternative. Rather thanguiding or soothing, it allows internal content to emerge undisturbed. If the goal is authenticintrospection and lasting transformation, then silence may be more supportive than sound.Future research should directly test this, shifting the burden of proof onto any setting feature,especially music, that claims therapeutic value.
This article explores a new type of publishing project — literary NFTs (non-fungible tokens). The concept of «literary NFTs» is defined, and the features of using non-fungible tokens in contemporary book publishing are examined. The article presents examples of the first NFT-based publishing houses, non-fungible editions, and digital platforms built on blockchain technology, analyzing their impact on the publishing industry. The study addresses both the technological and cultural dimensions of integrating NFTs into publishing, including issues of decentralization, authorship, digital uniqueness of texts, and audience engagement.
Andrea Esposito, Francesco Rossi, Marco Bernardo, Francesco Fabris · 5 authors
Algorand is a scalable and secure permissionless blockchain that achieves proof-of-stake consensus via cryptographic self-sortition and binary Byzantine agreement. In this paper we present a process algebraic model of the Algorand consensus protocol with the aim of enabling formal verification. Our model captures the behavior of participants in terms of the structured alternation of consensus steps toward a committee-based agreement. We validate the correctness of the protocol in the absence of adversaries and then extend our model to assess the influence of coordinated malicious nodes that can force the commit of an empty block instead of the proposed one. The adversarial scenario is analyzed through an equivalence-checking-based noninterference framework that we have implemented in the CADP verification toolkit. In addition to highlighting both the robustness and the limitations of the Algorand protocol under adversarial assumptions, this work illustrates the added value of using formal methods for the analysis of consensus algorithms within blockchains.
With the widespread application of blockchain technology, the security of private transactions has become a bottleneck restricting further development. This project presents a blockchain privacy transaction optimization model utilizing zero-knowledge proof (ZKP). By extracting data features such as transaction volume, transaction frequency, and counterparty trustworthiness, the model dynamically assigns weights through an entropy-based framework for different transaction scenarios. It also adaptively modifies certificate generation and verification strategies using reinforcement learning to enhance efficiency and security. In terms of experiments, a blockchain simulation environment is constructed, and 100,000 transaction data points are used as samples to compare the DA-ZKP algorithm and the traditional zero-knowledge proof algorithm. The experimental results show that the DA-ZKP algorithm reduces the generation time by 35%, the verification time by 28%, and the memory overhead by 22% on average. At the same time, the algorithm has a privacy protection capability comparable to traditional algorithms and can resist replay and tampering attacks. The optimization model and algorithm proposed in this project can effectively improve the efficiency and security of blockchain privacy transactions and provide a new idea for developing blockchain privacy protection technology.
The rapid development of the Internet of Things (IoT) has intensified security and privacy challenges across data generation, transmission, and storage. This study introduces a blockchain-based secure edge storage model tailored for IoT environments and presents a lightweight consensus algorithm, Deep Q-Network (DQN)-Raft+, which incorporates deep reinforcement learning. By combining the decentralized features of edge computing and blockchain, the model enables automated data access control through smart contracts. Furthermore, it optimizes leader node selection in the Raft consensus process using a DQN, formulating the consensus as a Markov Decision Process to enhance responsiveness and privacy protection in dynamic network conditions. Experiments were performed in a simulated environment using TensorFlow 2.6 and a MySQL database. The performance of DQN-Raft+ was compared against traditional consensus algorithms, including Proof of Work, Proof of Stake, Practical Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. Results indicate that DQN-Raft+ significantly reduces block generation delay (175.77 ms) and achieves a high privacy protection score (0.95). It also maintains a low data loss rate of 0.01%, demonstrating enhanced robustness and real-time capability. These findings indicate that DQN-Raft+ effectively strengthens data security and privacy in IoT systems, offering a technically sound and efficient mechanism for secure data exchange. The study provides both a theoretical framework and practical direction for future research in secure IoT deployment.
Kriti Patidar, Swapnil Jain, Mohammad Husain, Mohd Muqeem · 9 authors
As IoT-connected devices, sometimes referred to as the Internet of Things (IoT), continue to proliferate, existing centralized identity management systems struggle in the large scale due to issues with scalability, privacy and security. For these reasons, centralized identity management systems will not meet the requirements of large-scale IoT deployments. In this paper, we suggest a decentralized identity management system to authenticate and authorize IoT devices based on a hybrid blockchain and Zero-Knowledge Proof (ZKP) protocol. The proposed system utilizes decentralized identifiers (DIDs), verifiable credentials (VCs) and a hierarchical web-of-trust structure as part of the identity management process. The identity and credentials can be created and validated in a decentralized manner and locally, using smart contracts and lightweight consensus models such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The performance evaluation demonstrated the performance in respect of authentication latency businesses managed to get the latency to 250 ms, throughput reaching to 200 messages per second and energy efficiency improved to 300mW/device. Based on the baseline comparisons including PoW, OAuth and Hash-MAC based systems included, the proposed method is scalably better, provides greater security against DDoS and MITM attacks and used less memory. The proposed method yields a robust, fully decentralized identification system for managing IoT identities without requiring a centralized authority, allowing scalable and secure interactions across distributed networks.
In the rapidly evolving landscape of digital marketing and electronic commerce, short-form content—particularly on platforms like Twitter (now X)—has become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMs—LLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chat—guided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals.
Decentralized Finance (DeFi) represents the new generation of blockchain financial services by developing an open-access financial model without banking or lending institution intermediaries. However, DeFi's open feature threatens its security, making it vulnerable and a target for different attack types. In this systematic review, we present the security of DeFi by selecting fifteen studies from 2020 to 2024 to determine and display the security solutions' effectiveness in identifying the attacks, focusing on various DeFi components such as smart contracts, DEX, AMM, governance, AMM-based DEX, and smart contracts with (DEX, Oracle); detecting different kinds of attacks (e.g., price manipulation, Oracle manipulation, flash loan) using detection tools (e.g., DeFort, CRPWarner, FORAY); we find out that 40% of the selected studies focus on Oracle manipulation attack, 33.33% for price manipulation and flash loan attacks separately, followed by 13.33% for (MEV, rug pull, front-running, Token Leakage, and deep logical bugs), 6.67% for (EEV, reentrancy, sandwich, access control, and state derailment defects). We compare the studies based on the attack type that they detected using four state-of-the-art types of research, such as DeFiScope, FlashSyn, SecPLF, and DeFiGuard; this indicates the concentration of the trend studies is on accuracy and combining AI in DeFi security, or aggregating the existing tools with it, giving an overview of DeFi components' security, underlining the gaps in the attack types that future research can address to build more robust, trustworthy, and secure DeFi systems.
The swift advancement of technology has transformed numerous sectors, particularly the financial services business, with Decentralised Finance (DeFi) emerging as a notable disruptor. To guarantee the sustained development and integration of DeFi, it is necessary to investigate and comprehend the emerging trends in this field. This study presents a bibliometric analysis of 181 Decentralized Finance articles published from 2010 to 2024 in the Scopus database The data were examined and illustrated utilising the VoS viewer platform and R software, yielding both descriptive and visual insights. Authored by 503 researchers across 418 universities, these works span 129 journals and cite 10,428 references. Publications show an annual growth rate of 29.2 %, with 2024 exhibiting unprecedented output. Frontiers in Blockchain leads with six publications, followed by the Journal of Risk and Financial Management. New Zealand tops the citation rankings, led by Auckland University with 338 citations. Bellavitis C. and Chen Y. are the most prolific authors each with 338 citations. The cluster analysis identifies six thematic areas, offering insights into various aspects of decentralized finance. This study offers critical insights for academics, policymakers, and industry practitioners by mapping DeFi's transition from conventional financial systems to decentralized ecosystems. The findings illuminate research gaps, propose future research avenues, and underscore the necessity of developing policies and cyber hygiene protocols to mitigate the risks of decentralized finance. This work thus serves as a valuable resource for advancing the discourse on DeFi and its implications for financial innovation.
Stergios Intzes, Georgios D. Papadopoulos, Lykourgos Magafas
Our analysis focuses on the stock cryptocurrency market, by studying a group of nineteen cryptocurrencies where their capitalisation is about 99% of the total market. Specifically, it is examined this group of cryptocurrencies for the period from 2017 up to 2024, taking into account the effect of COVID-19 pandemic. As far as we know, this is the first time that this kind of study has been published, where it takes place by creating various networks based on stock cryptocurrency correlation, in order to be possible to visualise and access these relationships using methods from Complex Networks. The evaluation results show that, there are three different communities within the crypto market and although COVID-19 pandemic and ongoing geopolitical changes, there is a notable trend towards an increase in the number of cryptocurrencies. Additionally, it is applied discriminant analysis to identify the differences among various cryptocurrencies based on their features.
As blockchain technology continues to evolve, the security of smart contracts has increasingly drawn attention from both academia and industry. The Move language, with its unique resource model and linear type system, provides a solid foundation for the security of digital assets. However, smart contracts still face new security challenges due to developer programming errors and the potential risks associated with cross-module interactions. This paper systematically analyzes the limitations of existing security tools within the Move ecosystem and reveals their unique vulnerability patterns. To address these issues, it introduces MoveScanner, a static analysis tool based on a control flow graph and data flow analysis architecture. By incorporating cross-module call graph tracking, MoveScanner can effectively identify five key types of security vulnerabilities, including resource leaks, weak permission management, and arithmetic overflows. In terms of design, MoveScanner adheres to a modular principle, supports bytecode-level analysis and multi-chain adaptation, and introduces innovative resource trajectory tracking algorithms and capability matrix analysis methods, thereby significantly reducing the false positive rate. Empirical results show that MoveScanner achieved 88.2% detection accuracy in benchmark testing, filling the gap in security tools in the Move ecosystem. Furthermore, this paper identifies twelve new types of security risks based on the resource-oriented programming paradigm and provides a theoretical foundation and practical experience for the development of smart contract security mechanisms. Future work will focus on combining formal verification and dynamic analysis techniques to build a security protection framework covering the entire contract lifecycle
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
Haoxiang Luo, Ruichen Zhang, Yinqiu Liu, Gang Sun · 6 authors
Low-altitude airspace is becoming a new frontier for smart city services and commerce. Networks of drones, electric Vertical Takeoff and Landing (eVTOL) vehicles, and other aircraft, termed Low-Altitude Economic Networks (LAENets), promise to transform urban logistics, aerial sensing, and communication. A key challenge is how to efficiently share and trust the computing utility, termed “computility”, of these aerial devices. We propose treating the computing power on aircraft as tokenized Real-World Assets (RWAs) that can be traded and orchestrated via blockchain. By representing distributed edge computing resources as blockchain tokens, disparate devices can form Low-Altitude Computility Networks (LACNets), collaborative computing clusters in the sky. We first compare blockchain technologies, non-fungible tokens (NFTs), and RWA frameworks to clarify how physical hardware and its computational output can be tokenized as assets. Then, we present an architecture using blockchain to integrate aircraft fleets into a secure, interoperable computing network. Furthermore, a case study models an urban logistics LACNet of delivery drones and air-taxis. Simulation results indicate improvements in task latency, trust assurance, and resource efficiency when leveraging RWA-based coordination. Finally, we discuss future research directions, including AI-driven orchestration, edge AI offloading and collaborative computing, and cross-jurisdictional policy for tokenized assets.
In the paper, we consider multilevel multitask analysis of cryptocurrency news using a fine-tuned Mistral 7B large language model with retrieval-augmented generation (RAG). On the first level of analytics, the fine-tuned model generates graph and text summaries with sentiment scores as well as JSON representations of summaries. Higher levels perform hierarchical stacking that consolidates sets of graph-based and text-based summaries as well as summaries of summaries into comprehensive reports. The combination of graph and text summaries provides complementary views of cryptocurrency news. The model is fine-tuned with 4-bit quantization using the PEFT/LoRA approach. The representation of cryptocurrency news as knowledge graph can essentially eliminate problems with large language model hallucinations. The obtained results demonstrate that the use of fine-tuned Mistral 7B LLM models for multilevel cryptocurrency news analysis can conduct informative qualitative and quantitative analytics, providing important insights.
This study investigates the impact of digital currencies (including central bank digital currencies [CBDCs], cryptocurrencies, and Ethereum) on monetary policy effectiveness, specifically focusing on inflation-targeting success and financial stability. Using Autoregressive Distributed Lag (ARDL) modelling on monthly global data spanning January 2010 to December 2024, the empirical findings demonstrate that digital currencies significantly improve monetary policy outcomes. The results indicate that CBDCs and Ethereum transactions notably enhance inflation-targeting success, enabling central banks to better achieve targeted inflation through improved transaction efficiency and transparency. Ethereum also consistently demonstrates a stabilising impact on financial stability by reducing inflation volatility. Conversely, cryptocurrencies exhibit mixed impacts, suggesting potential speculative disruptions. The error-correction mechanisms highlight robust short-run adjustments towards equilibrium, supporting the reliability of the ARDL approach. These findings emphasize the need for policymakers to strategically integrate digital currencies into monetary policy frameworks, and recommend enhanced regulatory oversight, strategic adoption of Ethereum technology, and careful management of monetary growth and velocity of money to sustain economic stability.
In a time of growing environmental issues and climate change, the drive toward sustainability is more important than ever. Startups and small businesses are expected to be more instrumental in forming a sustainable future as world economies move toward greener paradigms. For many of these businesses, though, the financial load related to sustainable infrastructure, eco-innovation, and clean technology still be a major obstacle. For sustainable businesses trying to bring environmentally friendly ideas to market without sacrificing financial viability, green financing options including grants, subsidies, and green loans provide essential lifelines. Emphasizing the need of access to specific funding resources that support environmentally friendly practices, this abstract investigates the several green financing options open to startups. Examining both public and private sector projects emphasizes how green finance closes the innovation gap with implementation, especially for early-stage businesses trying to scale their green solutions. Grants and subsidies represent among the most well-known sources of green money. Usually governments, international organizations, and environmental NGOs supply these financial support to inspire creativity in fields including waste management, green manufacturing, sustainable agriculture, and renewable energy. Grants are a great choice for startups with limited cash flow since they unlike loans do not demand repayment. Many environmental grantinitiatives to support clean tech development have been started in areas including the European Union, North America, and portions of Asia. As part of the EU's larger goal to reach net-zero emissions by 2050, the European Green Deal, for instance, provides billions in support to sustainable businesses. To lower the initial costs of green investments, numerous local and national governments also provide direct subsidies and tax breaks. These could include financing for research and development of low-carbon technologies, subsidies for fleets of electric vehicles, or rebates for solar panel installations. In addition to fostering the growth of green startups, these policies hasten the market uptake of sustainable goods and services. Green loans have become a powerful instrument for sustainable finance in addition to grants. These are loans specifically designated for environmentally beneficial projects, and they frequently have favorable conditions like reduced interest rates, extended payback periods, or repayment plans that are based on performance. To assist with climate-resilient projects, organizations such as the World Bank, the Green Climate Fund, and several green investment banks provide specialized green loan programs. In order to specifically serve small and medium-sized businesses (SMEs) with environmental missions, some commercial banks have also entered this market by introducing green loan portfolios. Accessing green loans or grants for startups in need of these funds necessitates both a strong business plan and an unambiguous proof of environmental impact. The majority of funding organizations assess applications using standards like energy efficiency, circularity, social sustainability, and carbon footprint reduction. Thus, it is essential to have solid environmental metrics and data to support assertions. Furthermore, obtaining certifications such as B-Corp status or compliance with ESG (Environmental, Social, and Governance) standards can boost one's credibility and chances of getting funding. Additionally, startups now have more opportunities to interact with mission-driven investors who value sustainability in addition to financial returns thanks to the growth of impact investing. Green-minded venture capital firms and angel investors frequently offer seed money to eco-innovative companies, seeking high-growth prospects in line with long-term environmental objectives. Additionally, by reaching out to eco-aware communities, crowdfunding websites such as Kickstarter and Indiegogo are being used to fund green startups. Notwithstanding these encouraging advancements, obstacles still exist. Many startups are not equipped with the knowledge, skills, or resources necessary to successfully negotiate the intricate world of green finance. Grant and loan application procedures may be extremely competitive and cumbersome. Additionally, global scalability is hampered by the uneven distribution of green funding across various regions. Governments, financial institutions, and the private sector must work together more closely to close these gaps in addition to implementing policy changes and raising entrepreneur financial literacy. To address these challenges, startup incubators, accelerators, and advisory organizations are increasingly offering green finance consulting services, helping early-stage companies identify suitable funding options, prepare compelling applications, and build investor-ready sustainability strategies. Digital tools and platforms are also emerging to match green startups with appropriate funding sources, thereby streamlining the connection between innovative ideas and capital. In conclusion, green financing is not merely a niche category of economic support; it is an essential enabler of the global transition toward a more sustainable economy. By making green finance more accessible, equitable, and aligned with the realities of early-stage startups, stakeholders can unlock a wave of innovation that tackles some of the world’ s most pressing environmental issues. Whether through grants, subsidies, green loans, or impact investing, the opportunities for sustainable entrepreneurship have never been more abundant, but seizing them requires a well-informed, strategic, and purpose-driven approach.