Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. This study presents an AI-assisted framework for auditing crash data integrity by detecting potentially mislabeled records related to construction zone (czone) involvement. A GPT-3.5 model was fine-tuned using a fusion of structured crash attributes and unstructured narrative text (i.e., multimodal input) to predict work zone involvement. The model was applied to 6400 crash reports to flag discrepancies between predicted and recorded labels. Among 80 flagged mismatches, expert review confirmed four records as genuine misclassifications, demonstrating the framework’s capacity to surface high-confidence labeling errors. The model achieved strong overall accuracy (98.75%) and precision (86.67%) for the minority class, but showed low recall (14.29%), reflecting its conservative design that minimizes false positives in an imbalanced dataset. This precision-focused approach supports its use as a semi-automated auditing tool, capable of narrowing the scope for expert review and improving the reliability of large-scale traffic safety datasets. The framework is also adaptable to other misclassified crash attributes or domains where structured and unstructured data can be fused for data quality assurance.

As the global deployment of fifth-generation (5G) networks matures, the research community is conceptualising sixth-generation (6G) systems, projected for deployment around 2030. This article presents a comprehensive, evidence-based examination of the technological innovations and applications that characterise this transition, informed by a scoping review of 57 sources published between January 2020 and August 2025. The transition to 6G signifies a fundamental transformation from a mere communication utility to an intelligent, sensing, and globally integrated cyber-physical continuum, propelled by a strategic reassessment of the network’s societal function and the practical insights gained from the 5G era. We critically analyse the foundational physical layer technologies that facilitate this vision, including Reconfigurable Intelligent Surfaces (RIS), Terahertz (THz) communications, and the transition to Extremely Large-Scale MIMO (XL-MIMO), emphasising their interdependencies and the fundamental shift towards near-field physics. The analysis encompasses the architectural transformation necessary to address this new complexity, elucidating the principles of the AI-native network, the seamless integration of Non-Terrestrial Networks (NTN) into a cohesive three-dimensional framework, and the functional convergence of communication and sensing (ISAC). We also look at how these changes affect the real world by looking at data from trials and case studies in smart cities, intelligent transportation, and digital health. The article synthesises the overarching challenges in security, sustainability, and scalability, arguing that the path to 6G is defined by two intertwined grand challenges: building a trustworthy and sustainable network. By outlining the critical research imperatives that stem from these challenges, this work offers a holistic framework for understanding how these interconnected developments are evolving wireless networks into the intelligent fabric of a digitised and sustainable society.

Objective: Dementia is a prevalent neurological disorder, and accurate detection is essential for timely intervention. This study aims to design an interpretable symbolic feature engineering framework for EEG-based dementia classification and to examine how its performance depends on the validation protocol. Methods: We used a publicly available EEG dementia dataset including Alzheimer’s disease (AD), frontotemporal dementia (FD), and healthy controls. A self-organized explainable feature engineering (SOXFE) model was introduced with five phases: (i) feature extraction using Bipartite Channel Transformer (BCT) and Previous-Next Pattern (PNPat), (ii) feature selection via cumulative weight-based iterative neighborhood component analysis (CWINCA), (iii) explainable output generation using Directed Lobish (DLob), (iv) classification through an ensemble tkNN classifier, and (v) information fusion with combinational majority voting (CMV). Performance was evaluated with segment-wise 10-fold cross-validation, leave-one-subject-out (LOSO) cross-validation, and nested participant-grouped 10-fold cross-validation. Results: Segment-wise 10-fold cross-validation, which allowed segments of one participant in both training and test folds, yielded accuracies of 99.12–99.74%. Participant-independent accuracy was substantially lower: 51.19–63.81% for LOSO and 49.00–62.50% for nested participant-grouped validation at the segment level. Participant-level LOSO accuracy equaled the majority-class rate for dementia versus healthy controls (67.05%) and fell below it for AD versus FD (57.63% versus 61.02%). No exact McNemar comparison showed an accuracy advantage over the majority-class baseline. DLob outputs were produced as feature-level visualizations. Conclusions: The findings support the feasibility of interpretable, algorithm-derived symbolic feature representations. They do not establish reproducible disease-related EEG characteristics or clinical diagnostic utility. DLob outputs describe the channel transitions used by the model. Independent cohorts and stronger participant-independent performance are required before clinical application.