This paper examines the paradoxical (post-)growth trajectory of Bitcoin, the first âcryptocurrencyâ, as a case of infrastructural change in digital finance. Bitcoin's founding phase revolved around the principles of self-governance and self-limitation, which combined to create a commitment to degrowing the financial system and limiting monetary production to impede accumulation. Yet growth logics soon began to unfold after Bitcoin's creation in 2009. How and why did that shift occur, and with what implications? We rely on white papers and outputs of alt-coin founders to trace the socio-technical relations underpinning the emergence and expansion of âalt-infrastructuresâ oriented around growth. We demonstrate how what was originally designed as a post-growth infrastructure largely, albeit not fully, succumbed to conventional growth dynamics over a fairly short period.
With the development of artificial intelligence, blockchain, and metaverse technologies, the forms of virtual property have shifted from traditional types such as game items and social accounts to novel virtual property like non-fungible tokens (NFTs), metaverse land, digital artworks, and algorithmically generated content. Although Article 127 of the Civil Code calls for protection of virtual property, it does not clarify the legal character or ownership of such property. Legal protection for virtual property currently faces core dilemmas including ambiguous rights-holders, difficulty in identifying the objects of rights, and the absence of a registration and public-notice system. This paper argues that novel virtual property should be recognized as a form of âspecial property right.â Drawing on foreign experience and adopting a phased approach to legislation, China should construct a three-dimensional protection framework centered on registration and public notice. Such measures would enrich property-rights theory, safeguard user interests, and promote the healthy development of the digital economy.
This study aims to examine the development of research on the topic of âIslamic Smart Contractsâ and potential research plans based on journals published on this theme. This research uses a qualitative method with a bibliometric analysis approach. The data used is secondary data on the theme âIslamic Smart Contractâ from the Scopus database, with a total of 74 journal articles. The data was then processed and analyzed using the VosViewer application to map the bibliometric development of âIslamic Smart Contractâ research worldwide. The results of the study found 5 clusters with the most frequently used words being smart contract, technology, blockchain, contract, transaction, fintech, blockchain technology, and islamic finance. Then, the research path topics related to Islamic Smart Contracts are Blockchain for Islamic Finance, Fintech in the Islamic Finance Industry, Smart Contracts and Cryptocurrency in Islamic Finance, Smart Contract in Sukuk and Waqf, and Islamic Smart Contracts in Banking.
Devara S. Srinivas, Srinu Madem, Vijaya Kittu Manda, Theodore Tarnanidis
Blockchain technology can make a transformative impact on marketing and communications strategies. The technology has several applications, including tamper-proof influencer metrics, tokenized loyalty programs, and transparency in the community. A comprehensive analysis of recent academic literature and thematic content analysis helps identify theories connected to the topic and a practical implementation gap. Smart contracts enable verifiable transactions between stakeholders, while immutable ledger technology enhances brand credibility through authenticated communications. The chapter provides a structured implementation framework for marketing professionals, addressing technical integration requirements, resource allocation considerations, and anticipated return on investment. Generative AI offers new tools for content creation, while blockchain ensures its provenance and integrity. Challenges and future research directions are discussed. This work contributes to blockchain technology's theoretical understanding and practical application within an evolving Web3 paradigm.
Existing distributed ledger protocols either incur a high communication complexity and are thus suited to systems with a small number of processes (e.g., PBFT), or rely on committee-sampling-based approaches that only work for a very large number of processes (e.g., Algorand). Neither of these lines of work is well-suited for moderate-scale distributed ledgers ranging from a few hundred to a thousand processes, which are common in production (e.g, Redbelly, Sui). The goal of this work is to design a distributed ledger with sub-linear communication complexity per process, sub-quadratic total communication complexity, and low latency for finalizing a block into the ledger, such that it can be used for moderate-scale systems. We propose QScale, a protocol in which every process incurs only $\widetilde{O}(Îș\sqrt{n})$ communication complexity per-block in expectation, $\widetilde{O}(nÎș)$ total communication complexity per-block in expectation, and a best-case latency of $O(Îș)$ rounds while ensuring safety and liveness with overwhelming probability, with $Îș$ being a small security parameter.
Open access
2 source records
cs.DC
Complex Systems and Decision Making
Systems Engineering Methodologies and Applications
Privacy and security in Smart Cities remain at constant risk due to the vulnerabilities introduced by Internet of Things (IoT) devices. The limited computational resources of these devices make them especially susceptible to attacks, while their widespread adoption increases the potential impact of security breaches. This article presents a review of security proposals aimed at protecting IoT devices in Smart City environments. The review was conducted by analyzing recent literature on device-level security, with particular emphasis on lightweight cryptography, physically unclonable functions (PUFs), and blockchain-based solutions. Findings highlight both the strengths and limitations of current approaches, as well as the need for more practical, scalable, and resource-efficient mechanisms to ensure user privacy and data protection in IoT ecosystems.
Modern blockchain systems operating in adversarial environments require robust consensus protocols that guarantee both safety and termination under network delay attacks. Tendermint, a widely adopted consensus protocol in consortium blockchains, achieves high throughput and finality. However, previous analysis of the safety and termination has been done in a standalone fashion, with no consideration of the composition with other protocols interacting with it in a concurrent manner. Moreover, the termination properties under adaptive network delays caused by Byzantine adversaries have not been formally analyzed. This paper presents the first universally composable (UC) security analysis of Tendermint, demonstrating its resilience against strategic message-delay attacks. By constructing a UC ideal model of Tendermint, we formalize its core mechanisms: phase-base consensus procedure, dynamic timeouts, proposal locking, leader rotation, and others, under a network adversary that selectively delays protocol messages. Our main result proves that the Tendermint protocol UC-realizes the ideal Tendermint model, which ensures bounded termination latency, i.e., guaranteed termination, even when up to $f<n/3$ nodes are Byzantine (where $n$ is the number of nodes participating in the consensus), provided that network delays remain within a protocol-defined threshold under the partially synchronous net assumption. Specifically, through formal proofs within the UC framework, we show that Tendermint maintains safety and termination. By the composition theorem of UC, this guarantees that these properties are maintained when Tendermint is composed with various blockchain components.
This study aims to detect pump and dump (P&D) manipulation in cryptocurrency markets, where the scarcity of such events causes severe class imbalance and hinders accurate detection. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and advanced ensemble learning models were evaluated to distinguish manipulative trading behavior from normal market activity. The experimental results show that applying SMOTE greatly enhanced the ability of all models to detect P&D events by increasing recall and improving the overall balance between precision and recall. In particular, XGBoost and LightGBM achieved high recall rates (94.87% and 93.59%, respectively) with strong F1-scores and demonstrated fast computational performance, making them suitable for near real time surveillance. These findings indicate that integrating data balancing techniques with ensemble methods significantly improves the early detection of manipulative activities, contributing to a fairer, more transparent, and more stable cryptocurrency market.
Zeyu Chen, Yun Ji, Bowen Wang, Liwen Shi · 6 authors
Large language models (LLMs) show great promise in healthcare, but their applications are hindered by data privacy restrictions and the challenges of cross-institution collaboration. Sensitive medical data cannot be centralized, while non-independent and identically distributed (non-IID) characteristics across institutions further complicate convergence and fairness. To address these issues, we present a federated fine-tuning approach based on Low-Rank Adaptation (LoRA), enabling privacy-preserving knowledge flow across institutions. The method iteratively combines local LoRA adaptation with global parameter aggregation, allowing efficient knowledge sharing without exposing raw data. A blockchain identity scheme is used for identifying individual LLM in such a distributed network. We evaluate this approach on heterogeneous and highly non-IID medical text datasets, where experiments demonstrate that federated LoRA not only enhances cross-client generalization but also improves the performance of the weakest client, achieving stable convergence and fairer outcomes. These findings highlight federated LoRA fine-tuning as a practical and effective paradigm for adapting LLMs in healthcare, offering a new path for multi-center medical AI collaboration.
Hongbo Liu, Jiannong Cao, Bo Yang, Dongbin Bai · 10 authors
The rapid advancement of large language models (LLMs) in recent years has revolutionized the AI landscape. However, the deployment model and usage of LLM services remain highly centralized, creating significant trust issues and costs for end users and developers. To address these issues, we propose PolyLink, a blockchain-based decentralized AI platform that decentralizes LLM development and inference. Specifically, PolyLink introduces a decentralized crowdsourcing architecture that supports single-device and cross-device model deployment and inference across heterogeneous devices at the edge. Moreover, to ensure the inference integrity, we design the TIQE protocol, which combines a lightweight cross-encoder model and an LLM-as-a-Judge for a high-accuracy inference evaluation. Lastly, we integrate a comprehensive token-based incentive model with dynamic pricing and reward mechanisms for all participants. We have deployed PolyLink and conducted an extensive real-world evaluation through geo-distributed deployment across heterogeneous devices. Results indicate that the inference and verification latency is practical. Our security analysis demonstrates that the system is resistant to model degradation attacks and validator corruptions. PolyLink is now available at https://github.com/IMCL-PolyLink/PolyLink.
Mahalinoro Razafimanjato, Malik Muhammad Saad, Dongkyun Kim
The Internet of Vehicles (IoV), a critical component of Intelligent Transportation Systems (ITS), enhances driving safety and traffic efficiency through real-time data exchange. However, the dynamic and heterogeneous nature of IoV introduces significant security and trust challenges. To address these, trust management systems have emerged as vital mechanisms to ensure the reliability and integrity of data exchanged between vehicles. Blockchain technology offers a robust framework for addressing security and trust issues in IoV environments. The decentralized, tamper-resistant, and transparent nature of the blockchain makes it suitable for complex vehicular environments. This survey provides an overview of state-of-the-art blockchain-based trust management systems in IoV. Following a systematic literature review that filtered 8,280 publications to 63 core studies from 2019 to 2024, we present a thematic classification of existing solutions, focusing on those employing public and private blockchains. Unlike previous surveys, our work focuses specifically on the intersection of blockchain and trust management systems in IoV by analyzing approaches across four dimensions: trust computation methods, such as game theory and AI-driven models; blockchain scaling solutions, including sharding, sidechains, and optimized consensus mechanisms; integration with emerging technologies such as 5G/6G, Digital Twins, and Federated Learning; and security and privacy mechanisms. Finally, this survey identifies current challenges and provides future research directions, highlighting the need for more scalable, adaptive, secure, and privacy-preserving trust management systems in IoV.
The perishability of food products in the supply chain poses a significant challenge in ensuring quality and safety. Inefficient monitoring of temperature, humidity, and storage time results in substantial economic losses and increased health risks. Traditional traceability systems rely on manual audits or essential IoT platforms that lack predictive capabilities, leading to delayed anomaly detection and inefficient intervention. Blockchain-based solutions improve transparency but primarily focus on record verification rather than active anomaly detection and automated decision-making. This study proposes an integrated system combining Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain to optimize food traceability through real-time monitoring, predictive analytics, and secure decentralized record management. The system deploys smart sensors across storage and transportation units to continuously collect environmental data, which is processed by a deep learning model trained to detect deviations with 92.4 % accuracy. Detected anomalies trigger automated responses via smart contracts in a blockchain network, ensuring immediate corrective actions while maintaining immutable audit records. Results demonstrate a 64.3 % reduction in response time, improving reaction efficiency to critical storage failures. Additionally, false positive alerts decreased by 73.1 %, optimizing operational efficiency and minimizing unnecessary interventions. The blockchain implementation reduced storage overhead by 76.9%, ensuring scalability and long-term feasibility. This research establishes a foundation for intelligent, automated food supply chain management, demonstrating that integrating AI, IoT, and blockchain enhances safety, reduces waste, and optimizes logistics. Future work will focus on improvements in large-scale deployment and computational efficiency to refine this innovative approach.
The increasing integration of renewable energy into smart grids introduces challenges of demand-supply imbalance, peak load stress, and cyber-physical vulnerabilities. Existing demand response (DR) frameworks often lack scalability, privacy-preserving data sharing, and secure transaction mechanisms, which limit user participation and grid resilience. To address these challenges, this study proposes GridSyncNet, a blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response. The framework integrates federated learning to enhance decentralized forecasting accuracy, blockchain consensus to ensure transparent and tamper-proof energy trading, and actorâcritic based DRL agents to dynamically optimize load scheduling and energy dispatch across prosumers. Extensive simulations demonstrate that GridSyncNet outperforms benchmark models such as OD-CNN, D-FCAS, and USTCF. Specifically, it achieves a 98.2 % demand response efficiency, 30.6 % reduction in carbon emissions, and 97.4 % forecasting accuracy. Comparative analysis with multi-agent DRL (MADRL) approaches further confirms that GridSyncNet provides superior scalability, privacy, and security in decentralized environments. The proposed framework contributes to the design of secure, resilient, and sustainable energy management systems, offering practical insights for accelerating the transition toward net-zero energy communities. By combining blockchain, federated learning, and multi-agent reinforcement learning, GridSyncNet establishes a comprehensive pathway for trustworthy and adaptive smart grid operations. âą A multi-agent deep reinforcement learning framework for adaptive DR in smart grids. âą Decentralized peer-to-peer energy trading to transparent, secure energy trading. âą Renewable energy utilization 89 %, CO 2 reduction 30.6 % & forecasting accuracy 92.4 %. âą Federated learning & improved system resilience against cyber threats for DSM. âą Optimize load balancing, peak shaving & cost efficiency for distributed grid agents.
Gianmarco Di Palma, Roberto Scendoni, Davide Ferorelli, Anna De Benedictis · 6 authors
Background/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework. Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied. Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges. Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.
Open access
Artificial Intelligence in Healthcare and Education
DongâSeong Kim, Esmot Ara Tuli, Igboanusi Ikechi Saviour, Md Mehedi Hasan Somrat · 5 authors
Technological advancements have fostered ubiquitous connectivity, driving the adoption of cloud computing and cloud-based services and applications for maintenance and management purposes. Consequently, cloud computing and cloud-based services have gained popularity for their scalability and efficiency in maintenance and management. However, such interconnected systems are inherently vulnerable to cybersecurity threats, including unauthorized access, data breaches, and others. Blockchain technology, initially popularized through cryptocurrencies, has proven to be a robust and reliable solution for various applications beyond financial transactions, such as supply chain management and secure data sharing. The integration of blockchain with cloud computing has given rise to the concept of âBlockchain as a Serviceâ (BaaS), which provides developers with scalable and ready-to-use blockchain frameworks without the need for backend management. The current blockchain network has limitations such as low transaction per second (TPS), prolonged mining times, and other challenges that make it difficult for certain service-oriented applications. To address these issues, this paper introduces a hybrid on-off chain, edge-enabled BaaS solution called Pure Chain for service computing. Pure Chain refines the consensus algorithm, enhances the mining process, and enables seamless edge-supported online-offline hybrid transactions. Moreover, modified blockchain layers to improve efficiency and scalability. Pure Chain facilitates blockchain-enabled services and smart contract deployment in a cloud computing environment. Pure Chain optimizes energy consumption and delivers transaction speeds that are 12 times faster than conventional blockchain networks.
Introduction The development and growth of Asset-Backed Securitization (ABS) finance in the e-commerce supply chain is of great value in alleviating the capital constraints of enterprises. However, asset-backed securitization finance faces problems such as low cooperation efficiency and lack of trust mechanism. The consensus mechanism, encryption algorithm, traceability and other characteristics of blockchain can help strengthen cooperation between enterprises, improve the level of information sharing, and promote trust transmission. In the finance process, most managers are risk-averse, and managers are prone to overconfidence, overestimating returns and underestimating risks. Methods This paper studies asset-backed securitization finance in the e-commerce supply chain. Four models are constructed using game analysis: a non-cooperative model without blockchain, a cooperative model without blockchain, a non-cooperative model with blockchain, and a cooperative model with blockchain. The asset backed securitization finance strategy considering manager overconfidence and risk aversion with blockchain is analyzed to explore the optimal finance decision. Results and Discussion Through numerical analysis, it is found that blockchain helps to enhance the transparency and authenticity of financing information and increase the financing returns of e-commerce platforms. To a certain extent, the cooperation model can mitigate the negative impact of managersâ overconfidence on the financing returns of suppliers, e-commerce platforms and SPV. However, risk avoidance under the cooperative model is not conducive to the improvement of suppliersâ financing returns. The higher asset pool yield has significantly increased the financing returns of the SPV. The financing returns of suppliers and SPV will decline with the increase of the price sensitivity coefficient in the cooperative model, but it will be different in the non-cooperative model. This study strives to provide decision-making references for finance entities.
Lumu Emmanuel Roy Muyingo A, Brenda Nabachwa B, Faith Mugisha Ahabyoona C
This study evaluates the Parish Development Model (PDM) as a decentralized rural development initiative aimed at enhancing the economic empowerment of coffee smallholder farmers in Hoima District, Uganda. Using a convergent mixed-methods approach, the research integrates Senâs Capability Approach, Resilience Theory, and Social Capital Theory to examine three dimensions: institutional support, resource capacity building, and community engagement. Quantitative data were collected from 278 farmers through stratified random sampling, while qualitative insights were obtained from 12 key informant interviews and 8 focus group discussions. Findings indicate that while PDM has improved coffee yields by 37% among beneficiaries and strengthened trust in SACCOs with transformational leadership, significant barriers persist, including elite capture, gender-based land tenure disparities, and low uptake of climate-smart practices. Policy recommendations include community-vetted beneficiary selection, gender-responsive governance measures, and climate-resilient financing. The results offer lessons for scalable decentralized development models applicable in ASEAN and other Global South contexts.
Oxygen plays a central role in numerous industrial processes. Several technologies have been reported for producing oxygen from air. Among them, oxygen transport membrane (OTM) technologyâbased on mixed-conducting, gas-tight ceramic membranesâhas attracted significant attention due to its high oxygen selectivity, relatively low capital and operating costs, and versatility for both ex-situ and in-situ applications. Mathematical modeling of OTMs offers a powerful tool to investigate internal multi-physics transport phenomena, providing deeper insight into fundamental mechanisms while serving as a cost-effective approach for optimizing membrane stack designs. After a comprehensive introduction, in the first part of this dissertation (chapter 2), a comprehensive hollow fiber membrane model was developed, grounded in an experimental system as the physical basis. The model couples Multiphysics transport processes within the membrane with large-scale thermalâfluid transport in the test assembly and furnace. Systematic parametric studies are performed to investigate fundamental mechanisms and assess membrane performance. The results show that the oxygen partial pressure on the permeate side increases asymptotically from the inlet to the outlet of the hollow fiber membrane. In contrast, the longitudinal distribution of oxygen vacancy concentration decreases along the same direction, while the oxygen flux distribution follows the profile of oxygen vacancy concentration at the permeate surface. High feed-side air pressure and low permeate-side gas pressure are found to enhance oxygen permeation performance. Among the transport resistances, surface exchange resistance at the permeate side dominates, whereas bulk diffusion resistance contributes minimally in substrate-supported thin-film hollow fiber membranes. Overall, the modeling study provides valuable insight into the underlying mechanisms and offers practical guidance for membrane design and operation to improve oxygen production efficiency. To enable practical applications, upscaling from a single hollow fiber membrane to stacks and modules is essential. However, experimental methods for evaluating upscaling strategies are both time-consuming and costly. Mathematical modeling, by contrast, offers a cost-effective and flexible tool for this purpose. In the second part of the dissertation (chapter 3), building upon experimental results from a proof-of-concept hollow fiber membrane stack, a computational fluid dynamics (CFD)-based Multiphysics stack model was developed and validated. Extensive simulations were performed to examine stack behavior under varying operating conditions, and different design strategies were evaluated to optimize stack performance. The oxygen permeation process is thermally activated. Increasing the argon sweep gas flow rate lowers the oxygen partial pressure around the shell sides of the hollow fibers and enhances the permeation flux. Along the lumen side, oxygen partial pressure rises from the inlet to the outlet, with flux significantly higher in the upstream region than downstream. A distinct gradient of oxygen vacancy concentrations is observed across upstream fiber sections, from shell to lumen surfaces, but this gradient diminishes downstream. For a fixed stack length, adding more hollow fibers increases the overall permeation rate but reduces flux. Oxygen partial pressure decreases radially from the periphery to the center of the stack, leading to lower average flux in inner layers. An appropriate packing density is therefore required to achieve compact design while limiting pressure losses. For a given total fiber length, an optimal fiber number exists that maximizes average permeation performance. Diffusive oxygen flux dominates near fiber walls, while convective flux becomes increasingly important toward the fiber center. A higher sweep gas flow rate reduces the region where diffusion dominates. To maintain the elevated temperatures required for membrane operation, a high-temperature furnace is typically used. However, this results in low heating power efficiency and makes rapid temperature changes difficult due to the large volume of the furnace. Recently, a novel strategy has been employed in which external electrical power is directly applied to a hollow fiber membrane, enabling compact self-heating. To better understand the fundamental mechanisms, a mathematical model is developed in the third part of the dissertation (chapter 4) for a self-heated hollow fiber oxygen separation membrane, assisted by vacuum conditions applied at the lumen-side outlet. Comprehensive simulations are conducted to study the effects of self-heating on Multiphysics transport processes and oxygen permeation performance. Additional simulations are performed to investigate the influence of electrical field orientations applied to the hollow fiber membrane and the vacuum levels at the lumen-side outlet. The associated fundamental mechanisms are discussed and elaborated. A higher applied voltage increases the average membrane temperature. The longitudinal temperature profile is non-uniform, with a maximum in the mid-region and steep decreases toward both ends. Oxygen permeation flux rises with applied potential and is further enhanced by higher vacuum levels (lower permeate-side oxygen partial pressure). The flux distribution shows a domed shape along the fiber length, approaching zero near the ends. At low potentials, the effect of vacuum level is negligible but becomes significant at higher potentials. On the feed side, oxygen concentration decreases from the bulk to the surface, while on the permeate side it decreases from the surface toward the lumen outlet. Both gradients intensify with increasing voltage and/or vacuum level. Across the membrane bulk, oxygen vacancy concentration increases from feed to permeate surfaces, with steeper gradients under higher potentials and stronger vacuum. The orientation of the applied potential strongly influences performance. Alignment with the permeation direction, with positive and negative electrodes connected to permeate and feed surfaces respectively, greatly enhances flux. The opposite configuration suppresses it, while a perpendicular potential has little effect on radial ion transport. Building on these advances, Chapter 5 integrates the modeling and heating strategies into a Joule-heated hollow fiber ion transport membrane reactor for methane oxidative coupling (OCM). Results demonstrate that methane plays a dual role: it is both the feedstock for conversion and a promoter of oxygen transport by lowering surface oxygen partial pressure and sustaining higher vacancy-driven flux. The coupled transportâreaction model reveals that gradual, membrane-mediated oxygen delivery significantly improves Câ selectivity compared with conventional co-feed reactors. Methaneâvacancy interactions, heterogeneous surface reactions, gas-phase chemistry, and localized Joule heating jointly shape flux, product selectivity, and thermal profiles. This chapter demonstrates the dual functionality of MIEC membranes as both oxygen separators and catalytic reactors, providing a path toward intensified, energy-efficient chemical production. Finally, Chapter 6 synthesizes the insights, highlighting how Multiphysics modeling bridges the gap between laboratory observations and industrial-scale application. By clarifying oxygen transport mechanisms, optimizing stack designs, enabling compact Joule-heating strategies, and extending membranes into reactive processes, this dissertation contributes both fundamental knowledge and practical guidance. The results position MIEC hollow fiber membranes not only as efficient oxygen separators but also as versatile platforms for low-carbon energy, đ¶đâ management, and sustainable chemical synthesis.
Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future.
Birgit Altrichter, MariaLaura Di Domenico, Glenn Parry, Michael Rogerson
We conceptualize the centralizing-decentralizing paradox of blockchain implementation in supply chains and examine its consequences for complexity. Blockchainâs distributed ledger enables decentralized collaboration by securely sharing data. However, centralizing tendencies for focal firms to seek control over supply chains conflict with this opportunity. Our findings from blockchain for supply chain experts show widespread agreement that using blockchain facilitates decentralized collaboration whilst creating a supply chain systems paradox that often demands high levels of centralization. We find that reducing the paradox to a false dyad for communication masks its underlying complexity. We contribute to theory by developing a novel, nuanced conceptualization of underlying tensions of blockchain in supply chains. We conceptualize paradoxes as complex interacting tensions, advancing understanding of governance in blockchain-based supply chain management.