Abstract

Abstract
WebAssembly (Wasm) has emerged as a powerful binary format, enabling the seamless integration of languages like C and Rust into web applications. JavaScript (JS), the dominant language for client-side web development, has its code susceptible to tampering and intellectual property theft due to its transparency in browser environments. We introduce TranspileJS, a novel tool designed to enhance code security by automatically selecting and translating JS snippets into Wasm. TranspileJS leverages a multi-stage architecture that converts JS to TypeScript, which is compiled into Wasm using the AssemblyScript compiler. TranspileJS addresses the challenges posed by the fundamental differences between JS and Wasm, including dynamic typing, runtime behaviour mismatches, and standard library discrepancies, ensuring that the original behaviour of the code is preserved while maximising the amount of code transpiled. Our experiments show that TranspileJS successfully transpiles approximately one-third of the code in our dataset, with a performance impact of up to a 12.3% increase in execution time. The transpilation process inherently obfuscates code, creating effects similar to standard obfuscation techniques, and generates a stealthy and resilient output. Furthermore, combining transpilation with WebAssembly-specific obfuscation techniques opens new possibilities for code protection and resistance against reverse engineering.

Abstract
Renewable energy communities (REC) can involve final consumers into the energy system incentivizing investments in decentralized renewable energy sources and shaping their energy behaviour to improve the local balance of consumption and generation. However, RECs can also help alleviate energy poverty, which occurs when low incomes and inefficient buildings and appliances result in disproportionately high energy costs for households, by lowering energy expenses through the sharing of surplus electricity at reduced prices with vulnerable members. This work explores REC business models with the specific focus on incorporating and empowering vulnerable consumers. Based on the literature review, we propose indexes to assess the vulnerability and non-vulnerability of REC members. From these indexes, we propose two business models based on two different strategies for the operation and settlement of a REC with flexible assets and vulnerable members.

Abstract
The increase in the use of mobile phones in recent years has raised significant concerns regarding road safety, more specifically motorcycle accidents. Driver distraction caused by mobile phone use is a topic of great importance, given the negative impact on traffic safety and the occurrence of accidents. In this way, the design of optimized and strategically planned interfaces for electric motorcycle displays can play a crucial role in driver safety, minimizing distractions and, consequently, the risk of accidents. This paper proposes a systematic review of the literature on improving electric motorcycle displays in the context of mobile applications, exploring the best way for the display to become the only digital tool needed while driving, without causing distraction to the driver. To achieve this result, recent studies from the scientific literature were analyzed, highlighting the importance of a clean layout containing only relevant information to the visual elements. The interface structure should be easy to understand and recognize, with feedback always available so as not to overwhelm the user experience. Through the review, we studied how to improve the design of motorcycle displays and reduce the use of mobile phones while driving. The results were essential for us to understand that the usability of the interface is crucial for consistency in its structure, as well as the vocabulary, which must be coherent and in familiar language.

Abstract
The Circular Economy, an alternative to the linear make-use-dispose system, promotes sustainable development through novel business models. Thus, Circular Business Models emerge as systems that minimize resource input and waste by slowing, closing, and narrowing material and energy loops. Circular Startups play a crucial role in the transition to a Circular Economy. Despite their significance, there is a research gap in how these companies scale. Moreover, the slow transition is attributed to the limited scalability of Circular Business Models, which leads to the need to scale current practices. The present study aims to fill this gap by defining a typology of scalability strategies employed by circular startups. A qualitative case studies methodology is adopted, using document analysis and semi-structured interviews conducted in the context of the European project SoTecIn Factory. This research identifies five scalability strategies used by Circular Startups-impact, commercial, ecosystem, institutional and cultural-with the commercial strategy being the main focus in terms of growth approach. The findings underline a strong commitment across the observed value chains to minimize environmental impact, enhance social welfare, and foster economic growth. Other key findings reveal the presence of R-imperatives across different value chains, leading to industry-specific approaches. In addition to the theoretical contribution, this research can support sustainable growth by practitioners in their scaling efforts, thus, accelerating the circular transformation. © 2025 The Authors.

Abstract
The increasing integration of Artificial Intelligence (AI) across various sectors of society raises complex ethical challenges requiring systematic and scalable oversight mechanisms. While tools such as AIF360 and Aequitas address specific dimensions, namely fairness, there remains a lack of comprehensive frameworks capable of auditing multiple ethical principles simultaneously. This paper introduces a multidimensional AI auditing tool designed to evaluate systems across key dimensions: fairness, explainability, robustness, transparency, bias, sustainability, and legal compliance. Unlike existing tools, our framework enables simultaneous assessment of these dimensions, supporting more holistic and accountable AI deployment. We demonstrate the tool’s applicability through use cases and discuss its implications for building trust and aligning AI development with fundamental ethical standards.