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With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading‐edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5‐m blade section, and the results were analyzed in terms of operation speed and accuracy. Finally, future work is discussed to improve the performance of the system. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED. [ABSTRACT FROM AUTHOR]