Abstract
The automotive sector is undergoing continuous technological evolution driven by the demand for sustainable and safe vehicles. Among the main factors influencing safety, driver behaviour has been identified as a critical contributor to road crashes. This systematic review explores recent innovations in detecting risky driver behaviours, addressing six research questions: the most relevant datasets used for algorithm development and evaluation; system architectures and methodologies for anomaly detection; the most studied driver behaviours and related environmental, human, and mechanical factors; advances in machine learning, deep learning, and statistical methods; performance metrics and validation approaches; and the role of embedded technologies and sensors in practical applications. The review included 93 peer-reviewed articles published between 2020 and 2024, sourced from ACM, IEEE, ScienceDirect, and Scopus. Exclusion criteria were duplicates, non-open access, retracted works, and studies unrelated to outlier detection or driver behaviour. The Parsifal tool was used to support systematic data processing. Results highlight the most frequently used datasets, proposed models, and their performance in detecting driver behaviours, as well as the influence of contextual factors such as traffic rules, road conditions, and sensor limitations. Despite advances, real-world integration remains challenging, requiring further research and development. This review aims to guide researchers in understanding the current state of anomaly detection in driving contexts and to emphasize the need for broader collaboration to create effective, deployable solutions that enhance road safety worldwide.

Abstract
Artificial Intelligence (AI) integration in supply chain systems is growing, and with it grows its potential impact on inter-organisational collaborative networks. We review existing literature on how different AI archetypes (Reflexive, Anticipatory, Supervisory, Prescriptive) could support Collaborative Supply Chain Management (CSCM) activities, and how they impact information sharing, collaborative decision-making, and trust among supply chain partners at different integration levels. Adopting a sociotechnical perspective, we synthesise existing literature and map the archetypes along four levels of AI integration, varying in scope and decision autonomy. The results are conceptual frameworks demonstrating how AI impacts collaboration dynamics as it evolves from a decision-support tool to an autonomous coordination agent. Findings show differentiated effects along archetypes and integration levels, with implications for CSCM governance, transparency, and resilience. We contribute to the discussion on human-AI collaboration in CSCM and offer a baseline for research on the human-centric values of Industry 5.0.

Abstract
This review examines the vehicle communication systems, its evaluation measures, security concern and impact of contemporary technology. By making electronic searches through different databases, 20 articles were identified to include in the study. Findings have demonstrated that more sophisticated protocols are being implemented, e.g., FlexRay and Dedicated Short-Range Communication (DSRC), though older protocols, e.g., Controller Area Network (CAN) and Local Interconnect Network (LIN), remain widespread. Additionally, the use of Ethernet-based systems in automotive communications is increasing. However, many of these protocols have substantial vulnerabilities, which pose significant security challenges. The findings suggest adopting enhanced communication and security measures supported by Artificial Intelligence (AI) and Machine Learning (ML) for future vehicles. Overall, this work systematically evaluates in-vehicle communication protocols and proposes methods for addressing contemporary security challenges in the automotive industry.

Abstract

Abstract
Aesthetic outcomes in plastic and oncological surgery play a fundamental role in restoring patients' self-esteem, social engagement, and overall quality of life. Yet, managing pre-operative expectations and objectively assessing post-operative results remain as difficult challenges, compounded by the subjective nature of beauty and the scarcity of standardized evaluation tools. To address these challenges, we conduct a systematic review assessing computational methods for the prediction and evaluation of the aesthetic outcomes of plastic and oncological surgery, adhering to PRISMA guidelines. We propose a goal-oriented taxonomy that partitions computational approaches into two main categories: (1) prediction methods that pre-operatively predict the results of surgery through retrieval-based systems, generative artificial intelligence and advanced 3D modeling techniques, and (2) evaluation strategies that assess the post-operative outcomes through objective measurements, traditional machine learning, and deep learning models. Our synthesis indicates a potential paradigm shift from early work that relied on manual image annotation and manipulation to recent research that predominantly employs artificial intelligence. Nevertheless, over 90% of datasets remain private, and validation processes diverge among techniques with similar goals, limiting reproducibility and fair methodological comparisons. We conclude by advocating for the creation of larger publicly accessible datasets, integration of vision-language models to capture patient-reported outcomes, and rigorous clinical validation to ensure equitable, patient-centered care. By bridging computational innovation with clinical practice, this study contributes towards a more transparent, reliable, and personalized aesthetic outcome prediction and assessment.

Abstract
The increasing adoption of the Digital Twin concept and technology for managing complex physical assets has led to the emergence of Digital Twin Ecosystems, where interconnected digital twins generate additional value. However, ensuring seamless data sharing and interoperability among diverse systems presents significant challenges. Although research on digital twin architectures has advanced, gaps remain in addressing data governance, security, and stakeholders' trust. This study performs a comprehensive literature review to investigate architectural solutions to overcome challenges in digital twin ecosystems. The findings identify key requirements such as interoperability, governance, and data management, emphasizing the role of Data Spaces as enablers of secure data sharing. By structuring the requirements for digital twin ecosystem architectures, this paper identifies gaps suggesting future research on scalable and sustainable digital twin ecosystem implementations. These insights are expected to contribute to the development of frameworks that integrate technical advances with organizational and regulatory considerations, ultimately fostering the adoption of digital twin ecosystems across industries.