Diagnosis of autism spectrum disorder (ASD) significantly improves quality of life, yet current diagnostic practices rely on subjective behavioural assessments. While machine learning models using functional Magnetic Resonance Imaging (fMRI) show promise for objective diagnosis, existing approaches have critical limitations. High-accuracy models often neglect interpretability, increasing clinical distrust, while interpretable approaches frequently suffer from low accuracy and lack neuroscientific validation of identified biomarkers. Furthermore, no studies have systematically benchmarked which interpretability methods are most effective for fMRI data, hindering the translation of Artificial intelligence (AI) findings into clinical practice. In this observational study, we used the Autism Brain Imaging Data Exchange I (ABIDE I) dataset comprising 884 participants (408 with ASD; 476 controls with typical development) aged 7-64 years from five countries (Belgium, Germany, Ireland, the Netherlands, and the USA) across 17 international sites after applying mean framewise displacement (FD) filtering (>0.2 mm). Data were collected at the respective sites prior to the release of the ABIDE I dataset in August 2012. We described an explainable deep learning (DL) pipeline using a Stacked Sparse Autoencoder (SSAE) with a softmax classifier, trained on functional connectivity data. We systematically benchmarked seven interpretability methods using the Remove And Retrain (ROAR) technique and cross-validated our findings across three preprocessing pipelines. Critically, we validated identified biomarkers against independent neuroscientific literature from genetic, neuroanatomical, and functional studies. Filtering head movement (FD > 0.2 mm) increased classification accuracy from 91% to 98.2% (F1-score: 0.97), achieving state-of-the-art performance. ROAR analysis revealed gradient-based methods, particularly Integrated Gradients, as most reliable for fMRI interpretation. The model consistently identified visual processing regions (calcarine sulcus, cuneus) as critical for ASD classification. These findings aligned with independent genetic studies and neuroimaging research, confirming our model captured genuine neurobiological ASD markers rather than overfitting to dataset artifacts. We addressed three key research gaps by developing a highly accurate, interpretable ASD classification model, establishing a benchmarking framework for interpretability methods in the fMRI modality, and validating our identified biomarkers against established neuroscience literature. The consistent importance of visual processing regions suggests a fundamental biomarker potentially present across the ASD spectrum, offering new directions for diagnostic biomarker research, though translation to individual-level clinical applications requires further development. This work was supported by the Engineering and Physical Sciences Research Council, UK [EP/W524554/1].

Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation in inspections can reduce human effort and financial costs. Despite the benefits, research on automating inspection is sparse. This work proposes that OWT inspection can be described as a multi-robot coverage path planning problem. Reviews of multi-robot coverage exist, but to the best of our knowledge, none captures the domain-specific aspects of an OWT inspection. In this paper, we present a review on the current state of the art of multi-robot coverage to identify gaps in research relating to coverage for OWT inspection. To perform a qualitative study, the PICo (population, intervention, and context) framework was used. The retrieved works are analysed according to three aspects of coverage approaches: environmental modelling, decision making, and coordination. Based on the reviewed studies and the conducted analysis, candidate approaches are proposed for the structural coverage of an OWT. Future research should involve the adaptation of voxel-based ray-tracing pose generation to UAVs and exploration, applying semantic labels to tasks to facilitate heterogeneous coverage and semantic online task decomposition to identify the coverage target during the run time.

Exploring the underwater world presents significant physical and financial barriers. Telepresence technologies offer a potential solution by a providing a more accessible version of an underwater experience. Our research involved a scoping review to consolidate previous findings on vision-based technologies that aim to recreate underwater experiences and their user evaluations. We searched 5 academic databases for papers describing or evaluating technologies providing visual underwater experiences without actual submersion. We systematically searched YouTube to include immersive experiences not documented in academic publications. Our review included 45 academic papers and 23 YouTube videos classified by their level of ‘reality’, ‘degrees of freedom’, and presence of interactive elements. The technologies reviewed included virtual reality, 360 $$^{\circ }$$∘ video and imaging, augmented and mixed reality, head-mounted displays, mobile devices, cameras, sensors, remotely operated vehicles (ROVs), and games. Half of the selected papers featured user evaluations (with sample sizes ranging from 5 to 1006 participant); these methods included interviews, performance tracking, and questionnaires. We identified six main application areas for these technologies: (i) general environmental awareness, (ii) formal education about marine life, (iii) therapeutic interventions, (iv) access to underwater heritage sites, (v) ROV teleoperation and simulations, and (vi) entertainment. Immersive technologies, such as head-mounted displays and augmented reality, were prevalent across all application categories, though their usability varied. Cost considerations were also diverse, with costs ranging from expensive ROVs and simulated environments to cheaper 360 $$^{\circ }$$∘ videos. Our findings indicate a need for more robust user studies, including long-term research and comparisons among real-time, pre-recorded, and simulated experiences. A better understanding of entertainment-driven applications could benefit education, environmental conservation, and healthcare. The findings of the scoping review are discussed with respect to the technologies identified and the corresponding user studies.

Background: Social care systems worldwide face increasing demographic and financial pressures. This necessitates exploring innovative technological solutions to enhance service delivery without substantially increasing costs. Conversational interfaces, including interactive voice response, chatbots, and voice assistants, have gained traction as a means to improve accessibility and efficiency in social care. The rapid development of large language models such as ChatGPT has further accelerated interest in conversational artificial intelligence (AI). These technologies can offer intuitive interactions, particularly for individuals with limited digital literacy. However, their real-world impact, usability, and ethical considerations in social care remain underexplored. Objective: This scoping review aims to synthesize existing literature on the implementation, evaluation, and impact of conversational AI systems within social care settings for older adults. The review will identify best practices, current gaps, and future directions for research and implementation. Key research questions include the following: how are conversational systems implemented on a technical level, and how do older adults and their support systems use them in a social care context? What methods are used to evaluate acceptability, usability, and the impact of broad well-being in the context of older adults’ social care? and What are conversational technologies’ acceptability, usability, and well-being impact in the context of older adults’ social care? Methods: The review will follow the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and Population, Concept, and Context (PCC) frameworks. A systematic search will be conducted across five databases (IEEE, Web of Science, PubMed, ACM, and Scopus) for English-language articles published from 2019 onward. Studies will be included if they empirically examine conversational systems’ implementation, evaluation, or impact for older adults (aged ≥55 years) within a social care context. Two independent reviewers will screen articles and extract data. A descriptive analysis will then categorize findings across key domains such as accessibility, usability, ethical considerations, and well-being outcomes. Results: The results will be included in the scoping review, which began in March 2025. The analysis is underway and is expected to be completed and submitted for publication by September 2025. Conclusions: This scoping review will provide an overview of the role of conversational AI in social care, highlighting both opportunities and challenges in implementation. By synthesizing existing research, the review will inform future developments in the use of conversational agents to improve social inclusion, engagement, and well-being among older adults. International Registered Report Identifier (IRRID): PRR1-10.2196/72310

BACKGROUND Social care systems worldwide face increasing demographic and financial pressures. This necessitates exploring innovative technological solutions to enhance service delivery without substantially increasing costs. Conversational interfaces, including interactive voice response, chatbots, and voice assistants, have gained traction as a means to improve accessibility and efficiency in social care. The rapid development of large language models such as ChatGPT has further accelerated interest in conversational artificial intelligence (AI). These technologies can offer intuitive interactions, particularly for individuals with limited digital literacy. However, their real-world impact, usability, and ethical considerations in social care remain underexplored. OBJECTIVE This scoping review aims to synthesize existing literature on the implementation, evaluation, and impact of conversational AI systems within social care settings for older adults. The review will identify best practices, current gaps, and future directions for research and implementation. Key research questions include the following: how are conversational systems implemented on a technical level, and how do older adults and their support systems use them in a social care context? What methods are used to evaluate acceptability, usability, and the impact of broad well-being in the context of older adults’ social care? and What are conversational technologies’ acceptability, usability, and well-being impact in the context of older adults’ social care? METHODS The review will follow the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and Population, Concept, and Context (PCC) frameworks. A systematic search will be conducted across five databases (IEEE, Web of Science, PubMed, ACM, and Scopus) for English-language articles published from 2019 onward. Studies will be included if they empirically examine conversational systems’ implementation, evaluation, or impact for older adults (aged ≥55 years) within a social care context. Two independent reviewers will screen articles and extract data. A descriptive analysis will then categorize findings across key domains such as accessibility, usability, ethical considerations, and well-being outcomes. RESULTS The results will be included in the scoping review, which began in March 2025. The analysis is underway and is expected to be completed and submitted for publication by September 2025. CONCLUSIONS This scoping review will provide an overview of the role of conversational AI in social care, highlighting both opportunities and challenges in implementation. By synthesizing existing research, the review will inform future developments in the use of conversational agents to improve social inclusion, engagement, and well-being among older adults. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/72310