The rise of Non-Fungible Tokens (NFTs) and Internet of Things (IoT) devices created new demands for secure data management. To address these needs, we propose LIBLO, a lightweight blockchain-based smart NFT architecture designed for decentralized environments with limited resources. Traditional models mostly depend on heavy computation techniques to ensure the data security. To avoid this, LIBLO introduces a compressed blockchain layer combined with lightweight encryption techniques. This allows secure storage, verification, and controlled access to IoT-generated data without overloading devices. In this architecture, LIBLO acts as a trusted digital framework that securely encapsulates metadata, ownership identity, and access control policies. Each transaction is verified through digital signatures and efficiently recorded on a compressed blockchain ledger. This design ensures privacy, traceability, and integrity and also contributes to energy-efficient implementation in real-time IoT scenarios. Experimental results demonstrate that the suggested LIBLO achieves high encryption strength and strong decryption accuracy of 0.96%, and low error rates of (1.1%). By simplifying cryptographic operations and reducing blockchain complexities, LIBLO presents a practical and adaptable solution for securing digital assets and IoT interactions in smart environments.
The Internet of Things (IoT) is applied in various fields, and the number of physical devices connected to the IoT is increasingly growing. There are significant challenges to the IoT's growth and development, mainly due to the centralized nature and large-scale IoT networks. The emphasis on the decentralization of IoT's architecture can overcome challenges to IoT's capabilities. A promising decentralized platform for IoT is blockchain. Owing to IoT devices' limited resources, traditional consensus algorithms such as PoW and PoS in the blockchain are computationally expensive. Therefore, the PoA consensus algorithm is proposed in the blockchain consensus network for IoT. The PoA selects the validator as Turn-based selection (TBS) that needs optimization and faces system reliability, energy consumption, latency, and low scalability. We propose an efficient, lightweight blockchain for decentralizing IoT architecture by using virtualization and clustering to increase productivity and scalability to address these issues. We also introduce a novel PoA based on the Weight-Based-Selection (WBS) method for validators to validate transactions and add them to the blockchain. By simulation, we evaluated the performance of our proposed WBS method as opposed to TBS. The results show reduced energy consumption, and response time, and increased throughput.
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, DongâSeong 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 RÂČ 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.
The global cyberspace faces many cybersecurity challenges, including illegal changes to contract terms and loopholes in the smart contract framework itself. This study adopts the automatic execution and intelligence of digital contracts, uses public key infrastructure technology digital certificates to establish trust relationships, encrypts data, and improves network security; the distributed ledger adopts the Byzantine fault-tolerant algorithm to prevent data from being tampered with, solving the problems of low efficiency and low security of traditional cyberspace manual governance. The study shows that after 28 companies applied digital contracts in 2023, the average authenticity of the data was 50.95% higher than the average authenticity of the data in 2022, the average integrity of the data was 36.44% higher than the average integrity of the data in 2022, and the average security of the data was 110.22% higher than the average security of the data in 2022. The findings highlight the critical role of digital contract implementation in enhancing the security and operational efficiency of global cyberspace governance, offering an effective solution to address the complex challenges inherent in managing todayâs interconnected digital environment.
With the extensive growth in terms of data and AI adoption across various fields such as finance, healthcare and insurance, data security and privacy have become significant barriers to innovation. This paper provides a privacy-aware framework for distributed AI as a possible solution which is integrated with cloud-native architectures. By leveraging decentralized model training without sharing raw data, this solution offers a compliant and secure framework for deploying machine learning at scale. A scalable and cost-effective system architecture is proposed that aligns with data protection regulations while maintaining high performance and model accuracy. This approach empowers organizations to leverage AI responsibly, unlocking the potential of sensitive data without compromising privacy.
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 Kacâs 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 Kacâs 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 â€n, 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 research proposes and evaluates a blockchain-based AIoT inspection system implemented on an NVIDIA Jetson Xavier NX edge computing device. The system integrates AI-powered inspection capabilities with Hyperledger Fabric blockchain technology to ensure tamper-proof record-keeping and data integrity. Performance analysis demonstrates the feasibility of running distributed ledger technology in conjunction with AI processing on edge computing devices. The implementation achieves reliable verification of inspection data while maintaining acceptable computational resource usage and energy consumption. Results indicate that blockchain integration prevents unauthorized data manipulation without significantly compromising system performance.
The growing integration of microservices exacerbates the mounting concern over privacy and trust caused by frequent data leaks and breaches. These systems are inherently decentralized, making them harder to manage, increasing the attack surface, and complicating trust between independently deployed services. Despite these persistent challenges, trust and authentication are often handled using centralized and stateful methods that require storing extensive state information. Existing so-called stateless authentication methods typically shift state storage from the server to the client side rather than detaching from state dependency. To address this challenge, we propose a lightweight, scalable, and truly stateless authentication approach that is non-idempotent and utilizes self-resetting nested Zero-Knowledge Proofs (ZKPs) to eliminate reliance on past actions. This platform anticipates user intentions and rigorously monitors them, ensuring bidirectional operational integrity while preserving privacy. Specifically, the self-resetting ZKP mechanism leverages the dynamic nature of microservices and incorporates random proof segmentation as a random generator to ensure each request is unique. Our tests using the OpenTelemetry Demo confirm its functionality and the independence between requests. Furthermore, security analysis demonstrates comprehensive security through non-idempotent authentication and the uniqueness of proof segmentation for each request.
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 â up to 42.5 % by 2030; increasing energy efficiency â 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.
The rapid growth of Internet of Things (IoT) devices has revolutionized digital ecosystems, enabling real-time automation and decision-making. However, the large volume and sensitivity of data from distributed IoT networks pose serious challenges in ensuring secure, scalable, and trustworthy data sharing. Centralized architectures are prone to failures, breaches, and limited auditability. This paper proposes a blockchain-integrated framework to overcome these limitations in heterogeneous IoT environments. The model utilizes distributed ledger technology (DLT) to ensure tamper-proof data integrity, decentralized control, and verifiable audit trails. Smart contracts dynamically enforce access policies, while attribute-based encryption (ABE) provides fine-grained control over data access. Lightweight blockchain nodes are deployed at the edge to reduce latency and reliance on cloud infrastructure. A layered architecture ensures seamless interaction among IoT devices, edge nodes, and cloud systems. To evaluate the framework, a prototype was built using ESP32 sensors, Raspberry Pi 4 edge nodes, and a Hyperledger Fabric blockchain network. Data encryption and smart contract execution were tested under realistic conditions. Results show enhanced data security, access control, and system scalability with minimal performance overhead. This framework provides a practical solution for secure data sharing in IoT ecosystems and is well-suited for deployment in smart cities, healthcare, and industrial automation.
This paper proposes a dynamic, decentralized social trust model to meet Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance requirements, especially for individuals lacking formal documentation. Building on previous work, we present a conceptual model for dynamically computing trust and risk values to support financial transaction approvals through innovative recognition schemes. Agent-based simulations across four scenarios demonstrate that dynamic trust mechanisms enhance transaction approval rates and payment volumes, provided malicious agents are controlled. The model is flexible, adaptable to diverse populations, and promotes financial inclusion without compromising compliance requirements. It also offers a foundation for future research on mitigating risks from malicious actors. Furthermore, it is particularly suited for enabling compliant and inclusive decentralized finance (DeFi) applications.
Tunahan TÄ°MUĂÄ°N, SĂŒmeyye Bayrakdar, Ezgi Kara Timuçin, Muhammed Enes Bayrakdar
In this paper, a cognitive radio medical sensor network based on smart contract is proposed for secure communication in field hospitals. Field hospitals usually utilize cognitive radio based opportunistic networks for health data transmissions due to extraordinary sudden occasions. And, cognitive radio based opportunistic networks is prone to risks. So, security is a crucial factor for opportunistic networks in cognitive process such as spectrum sensing [1]. Secure data transmission is very important for patient related sensed data in field hospitals. Failure to transmit, or incorrect transmission of values such as vitamin levels, glucose levels in the blood can critically impact the treatment applied to the patient [2]. In our network structure, sensor nodes use the carrier sense multiple access (CSMA) technique for medical communication. Utilizing our smart contract based approach, security measures were taken in the field hospital environment. Since secure communication is crucial in field hospitals, a smart contract-based data sensing approach is proposed to enhance network security. Sensor nodes are placed to cover the whole region to sense parameters. The values sensed by the sensor nodes were composed by the base station. The simulation model of our proposed approach was implemented using Riverbed Modeler software. Owing to our proposed smart contract based approach for cognitive medical sensor networks, the field hospital environment was supervised securely with wireless sensor nodes.
In recent decades, the challenges of sustainable development and the urgency of accelerating the transition to cleaner energy matrices have renewed interest in the concept of Mission-Oriented Policies (MOP). Within this context, nuclear energy has regained legitimacy due to the emergence of a new generation of Small Modular Reactors (SMRs). This paper proposes a characterization of the main national strategies currently underway for the development of these reactors and provides a comparative analysis of how they align with the MOP approach. The analysis identifies two general models of technological and industrial policy related to the promotion of the nuclear industry: the centralized-autonomous model and the decentralized-associative model. The POM schemes differ in the role of the state in technological and energy planning, the degree of concentration and centralization of technological capacities and decision-making, and the coordination and organization of actors and innovation projects, among other aspects. Finally, the implications for semi-peripheral countries with emerging nuclear sectors, such as Argentina, are discussed, highlighting the importance of proposing hybrid models tailored to the characteristics of the national environment.
Science, Technology, and Education in Latin America
Psychedelic Minimalism: The Case Against Music in Psychedelic Therapy SettingsIn response to: Kaelen et al. (2018), âThe Hidden Therapist: Evidence for a Central Role ofMusic in Psychedelic Therapy.âIntroduction: 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 patientâs 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 itâs 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 patientâs 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 thepatientâs 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 patientâs 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 (âThe ReSPCT Guidelinesâ 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 âunder psilocybin I felt almost thatI had no choice but to go with the musicâ (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 wasâsuffocating,â and that the music was âtoo muchâ (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 themusicâs associative framework, not by the patientâs 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 musicâs 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, aâstartup providing music both for and as psychedelic therapyâ (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 patientâs 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 â 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.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Consensus mechanisms are essential in swarm robotics to maintain uniformity of decisions and states across distributed agents. Earlier methods often relied on approaches such as majority voting, averaging techniques, or leader election. In recent years, blockchain-based algorithms including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Raft have been adopted for this purpose. While these methods provide certain advantages, their application to swarm robotics is restricted by issues such as limited computational power, energy constraints, scalability challenges caused by message complexity and latency, and weak protection against Byzantine agents. This study introduces a consensus approach specifically designed to address these gaps and to improve collaborative decision-making in swarm environments. The work outlines the motivation for the proposed solution, describes the simulation-based experimental design, and presents a detailed analysis of the observed results.
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.