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The study focuses on the integrated use of multiscale and multisensor remote sensing techniques and big data analysis for the identification of buried archaeological remains or areas of potential archaeological interest. Satellite multispectral data (at very high and high resolution), drone based visible, multispectral, and thermal imagery, and geophysical prospecting (gradiometer) were used. The ancient city of Metaponto was chosen as case study, as it was a very important city in the formative panorama of Italian Magna Graecia and it also is one of the most important and best preserved archaeological sites in southern Italy. The analysis of remote sensing data from different sensors, with different resolutions, and referable to different physical parameters, allowed to deepen archaeological knowledge on a landscape scale, as well as on a site scale, going from the analysis of traces of the ancient landscape (e.g. palaeo-channels, canalisation system, main roads), to the discovery of small features (e.g. secondary roads, houses, facilities). [ABSTRACT FROM AUTHOR]

Most of the artistic heritage in the Mediterranean basin is hosted in rupestrian hypogeum whose peculiarity is given by the presence of at least one open side, which makes them particularly sensitive to meteorological conditions. This makes mandatory the monitoring of both indoor and outdoor environmental parameters to analyze the cause–effect relationship between microclimatic inside and outside the hypogeum. The paper proposes a spatial and temporal multi-scale methodological approach applied to a rupestrian church in Matera, which hosts precious wall paintings, particularly vulnerable to the effects of environmental parameters. The approach is based on the analysis of data acquired by three platforms: indoor, close-range outdoor, and outdoor data from a meteorological station and weather forecast from the COSMO 5 model. The method allowed to characterize the relationships between the indoor and outdoor parameters at different spatial and temporal scales. The results showed a significant correlation between the parameters, thus opening new opportunities for the monitoring of the rupestrian heritage based on the use of data systematically available, such as those from meteorological stations and meteorological forecast. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of modern healthcare, nurses must proficiently navigate data utilization and grasp the principles of data science. Despite this urgency, nursing stakeholders currently do not fully understand the extent of data literacy or data science literacy they need to acquire. This paper aims to elucidate the distinctions between data literacy and data science literacy, offering insights into strategies for nurturing these competencies within nursing education, research, and practice. Through a state-of-the-art review of 22 articles and six healthcare industry resources, we identified a notable absence of comprehensive frameworks and assessment tools, highlighting key areas for future development.

Introduction: The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption., Design: Qualitative descriptive study., Methods: Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke., Results: Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools., Conclusion: This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy., Clinical Relevance: This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support., (© 2024 The Author(s). Journal of Nursing Scholarship published by Wiley Periodicals LLC on behalf of Sigma Theta Tau International.)

The construction history of a site is partially preserved underground and can be revealed through archaeological investigations, including excavations, integrated with earth observation (EO) methods and technologies that make it possible to overcome some operational limits regarding the areal dimensions and the investigation depths along with the invasiveness of the excavations themselves. An integrated approach based on EO and archaeological records has been applied to improve the knowledge of Machu Picchu. The attention has been focused on the first construction phase of Machu Picchu, and for this reason the investigations were directed to the imaging and characterization of the subsoil of the Plaza principal, considered the core of the whole archaeological area. Archaeological records and multiscale remote sensing (including satellite, UAS, and geophysical surveys) enabled the identification and characterization of the first construction phase of the site, including the preparation phases before building Machu Picchu. The interpretative hypothesis on the constructive history of Machu Picchu started from the identification and use of the quarry, followed by the planification and set of the drainage systems and by the next steps based on diverse reshaping phases of what would be the central plaza., (© 2023. Springer Nature Limited.)

Objectives: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools., Methods: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework., Results: Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care., Conclusion: In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings., Competing Interests: The authors declare that they have no conflicts of interest in the research., (Thieme. All rights reserved.)