The increasing adoption of blockchain technology across industries highlights the need for adaptable and scalable solutions that lower technical barriers to entry. While blockchain offers security, decentralization, and transparency, existing frameworks often require specialized expertise, limiting widespread adoption. This study proposes FlexBoardChain, a novel, flexible, and democratic blockchain framework that facilitates seamless integration into various applications. Our approach addresses key challenges in smart contract implementation, including interoperability, modularity, and usability, by abstracting complexities and offering an accessible API. Through a literature review, we identified the limitations of existing blockchain frameworks. We designed FlexBoardChain to enhance economic viability and security while enabling non-experts to configure and deploy blockchain-based solutions effectively. We implemented a use case in a community-centric buy-sell group to validate our framework, demonstrating its applicability in realworld scenarios. Our findings suggest that FlexBoardChain may facilitate and foster broader blockchain adoption by providing a scalable, user-friendly, and adaptable solution tailored to industry needs.
Traffic congestion and vehicular emissions remain critical challenges in urban mobility. While reinforcement learning (RL) has shown promise in adaptive traffic signal control, conventional models may inadvertently encourage private vehicle use by merely reducing delay. In this study, we present a Q-learning-based traffic signal control framework enhanced with a vehicle prioritization mechanism for public transport and emergency vehicles. Implemented using the Simulation of Urban Mobility (SUMO), our approach is evaluated on a four-arm intersection scenario. Compared to fixed-time control, the standard Q-learning model achieves an $80 \%$ reduction in average vehicle delay and over $80 \%$ decrease in $C O_{2}$ emissions. The prioritized Q-learning variant further improves delay and emissions metrics while providing preferential treatment to high-impact vehicle categories. Crucially, this prioritization strategy helps incentivize public transport usage, mitigating the risk of increased private car dependence that often follows general congestion reduction efforts. Our results demonstrate that integrating vehicle prioritization into RL-based traffic control supports both sustainability and modal shift goals in intelligent transportation systems.
Digital Product Passports (DPPs) are emerging as foundational tools for transparency, traceability, and sustainability in global supply chains. As regulatory initiatives such as the European Union’s Ecodesign for Sustainable Products Regulation (ESPR) gain momentum, there is increasing demand for technical solutions that support decentralised management and retrieval of product lifecycle data. This paper proposes a lightweight, extensible architecture centred on the DPP Protocol—a general-purpose communication mechanism for interoperable access to product information. The approach is particularly suited to fragmented or highly variable production contexts, such as fashion and other sectors characterised by short product lifecycles, complex supply networks, and limited digital infrastructure. We present two complementary implementations: the DPP Software, a server-side system for managing product data, and the DPP Browser, a stateless client for visualising and querying digital passports. Together, these components demonstrate the feasibility and versatility of the proposed protocol. The solution emphasises interoperability, recursive querying, and role-based access control, offering a foundation for scalable adoption across diverse industrial and regulatory settings.
Industry 5.0 redefines industrial automation by emphasizing human-centricity, sustainability, and resilience. Within this paradigm, the Operator Digital Twin (ODT) has recently emerged as a digital counterpart of the human worker, integrating biometric, contextual, and behavioral data to enable adaptive interactions with machines. However, fully exploiting the potential of ODTs introduces significant challenges, including the dynamic management of AI functionalities, privacy preservation, and context-aware deployment across heterogeneous computing devices. This paper proposes a dynamic, privacy-aware framework for managing AI capabilities within ODTs. The approach supports real-time adaptation of machine learning models based on the operator’s condition and the computational constraints of devices such as smartphones and embedded systems. By leveraging edge processing, the architecture minimizes the exposure of sensitive biometric data while ensuring reliable functionality and compliance with privacy regulations. The framework is validated in a prototypical industrial testbed using real ML models and heterogeneous hardware, demonstrating its effectiveness in enabling context-driven and secure AI orchestration in ODT-enabled industrial environments.
The escalating complexity and length of online privacy policies pose a substantial obstacle to user comprehension, thereby undermining informed consent. While manual annotation efforts have improved transparency, they are inherently limited in scalability. To address this challenge, we introduce COAT (Comprehensive Online Agreement Transparency), a novel framework for the automated analysis and risk scoring of privacy policies using Large Language Models (LLMs). Within the COAT system, this paper presents a comparative study evaluating several LLMs, including OpenAI’s GPT series and open-source models. Our methodology benchmarks LLM performance against humanannotated privacy risk scores using a set of specific policy clauses. This study validates the feasibility of using LLMs for scalable, automated privacy policy evaluation and highlights the performance disparities among current models. To foster further research and evaluation, the resulting dataset of LLM-generated scores is made publicly available.
