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Emergence and development of the blockchain technology, which is able to transform into "a most powerful disruptive innovation", shall definitely concern universities. Moreover, nowadays the blockchain technology meets the challenges that both the system of higher education and the entire society are currently facing. Advantages of the blockchain technology are decentralized open data, absence of forgeries, safe storage of information, and reduction of transaction expenses related to data checkup, control, and verification. This paper provides a critical analysis of application of the blockchain technology considering with its applicability opportunities and restrictions in education; it also aims to identify the consequences of its influence upon the development of education. The article analyzes real cases when this technology was applied, with the Massachusetts Institute of Technology (MIT) as an example. The MIT applied it to protect and validate the certificates that it issued. Another example is the Sony Global Education that forms individual data on its trainees' competencies and productivity; a third one relates to the University of Nicosia, which was the first to use smart contracts and accept cryptocurrency as a form of payment. The paper also considers the elements of the blockchain technology at universities (both in Russia and outside it), which participate in massive open online courses. It determines the scope of application of this technology in the Russian educational system. In addition, this article provides a literature review related to application of the blockchain technology; the review includes works by such renowned researchers as D. Tapscott, B. Bleir, A. Watters, A. Grech, A. Camilleri, M. Swan, A. Zaslavsky, etc. The paper analyzes the obtained findings of the survey that its authors have conducted among experts, professors, and specialists involved in accreditation. Thus, the paper provides an analysis of opportunities and restrictions related to application of the blockchain technology in higher education.

Introduction: Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field. Methods: Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set. Results: DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations. Discussion: Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy. [ABSTRACT FROM AUTHOR]

Statement of Problem: Maxillofacial prosthodontists were advanced digital technology (ADT) adopters early in the new Millennium. The past two decades saw a range of digital enablers emerge including digital imaging (internal and surface), digital surgical planning, digital functional assessment, subtractive and additive manufacturing, navigation, and robotics among others. Artificial Intelligence (AI) is the latest ADT arrival that will be a challenging disruptive technology. ADT has served as a profound change agent in maxillofacial prosthodontics. The intent was to explore the process and level of ADT engagement in maxillofacial prosthodontics., Purpose: The purpose was twofold. Firstly, to explore maxillofacial prosthodontic engagement of ADT. Secondly, to develop a discussion document to assist the American Academy of Maxillofacial Prosthetics (AAMP) with establishing a collective awareness and considered opinion on the future of maxillofacial prosthodontics in the digital era., Material and Methods: AAMP member interest in ADT was assessed through analysis of AAMP annual congress programs and publications in the Journal of Prosthetic Dentistry (JPD). The history of the maxillofacial prosthodontic journey to the digital era was undertaken with a selective literature review. The perceptions maxillofacial prosthodontists hold on ADT engagement was assessed through a survey of AAMP members. Developing an understanding of the influence AI was conducted with a review of pertinent literature., Results: From 2011-2020, an annual mean of 38% of papers published in the JPD involved clinical use of ADT. From 2017-2019, 44% of invited presentations at AAMP annual congresses included clinical use of ADT. The journey to the digital era distinguished three periods with formative and consolidation periods influencing the innovation digital era. The AAMP member survey had a 59% response rate and studied 10 domains through 31 questions. Of the respondents, 89% thought ADT important to the future of maxillofacial prosthodontics., Conclusions: The discussion document will assist the AAMP in developing a collective consciousness and considered opinion on ADT in the future of maxillofacial prosthodontics. Members of the AAMP have a developed interest in clinical applications of ADT. A great challenge is that no formal education, training, or clinical competency requirements for ADT could be identified. Clinical competency requirements are important to prepare maxillofacial prosthodontics for the inevitability of a digital era future. The discussion document poses the fundamental question of whether maxillofacial prosthodontists will remain as passive end users of ADT and AI or will they become engaged knowledge workers that have determined clinical competency in ADT and AI in patient care. Without this knowledge worker role, maxillofacial prosthodontists may experience difficulty being part of the inevitable ADT-AI driven future., (Copyright © 2024 Editorial Council for The Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.)

In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to "improve the efficiency of all social systems," "create new industries," and "increase intellectual productivity." For the analysis, we used 19-year bibliographic data (2000-2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea., Competing Interests: The authors have declared that no competing interests exist.

RPA is used to complete repetitive tasks usually completed by humans. A robot is a software programed to mimic the operations of humans on computer systems. Robots are configured to accomplish these tasks using algorithms from the machine learning and artificial intelligence field. Using RPA leads to reduced errors in the processing of tasks and reduced completions times to carry out the tasks. This paper calculates the number of software licenses required to carry out thousands of transactions for financial services and proposes an assignment of the transactions to the robots. A two-phase linear integer optimization process is proposed. The first phase calculates the number of robots required to complete all of the tasks and the second phase assigns the tasks to the robots. Many different transaction types for financial services are to be completed, and each type has precise market operating hours, maximal delays in their treatment, different processing times and different volumes. The objective function minimizes the total number of robots, but also limits the number of robot startups and reconfigurations, since these operations take time to complete. The key behind the fast computing times to solve the problem is the formulation, where periods are defined at intersections of market operating hours. Preliminary tests are conducted on a real case provided by a bank in North America and a synthetic test case and show that the problem is solved quickly. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Teledermatology may be defined as the application of telemedicine to dermatology. According to published data, teledermatology is more widespread in Europe and North America, probably where resources for health care are greater than in other areas of the world. Indeed, teledermatology requires advanced technology to be efficient, as high image quality is necessary to allow the dermatologist to make correct diagnoses. Thanks to the recent advances in this field, teledermatology is become routinary in daily clinical practice. However, its use has been improved over time, overcoming several challenges. The aim of this narrative review is to retrace the almost 30-year history of teledermatology, to address the new challenges posed by advancing technologies such as artificial intelligence and the implications it may have on healthcare. [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm. [ABSTRACT FROM AUTHOR]

The European Society of Toxicologic Pathology (ESTP) initiated a survey through its Pathology 2.0 workstream in partnership with sister professional societies in Europe and North America to generate a snapshot of artificial intelligence (AI) usage in the field of toxicologic pathology. In addition to demographic information, some general questions explored AI relative to (1) the current status of adoption across organizations; (2) technical and methodological aspects; (3) perceived business value and finally; and (4) roadblocks and perspectives. AI has become increasingly established in toxicologic pathology with most pathologists being supportive of its development despite some areas of uncertainty. A salient feature consisted of the variability of AI awareness and adoption among the responders, as the spectrum extended from pathologists having developed familiarity and technical skills in AI, to colleagues who had no interest in AI as a tool in toxicologic pathology. Despite a general enthusiasm for these techniques, the overall understanding and trust in AI algorithms as well as their added value in toxicologic pathology were generally low, suggesting room for the need for increased awareness and education. This survey will serve as a basis to evaluate the evolution of AI penetration and acceptance in this domain. [ABSTRACT FROM AUTHOR]

Copyright of Review of Policy Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ocean warming endangers coastal ecosystems through increased risk of infectious disease, yet detection, surveillance, and forecasting of marine diseases remain limited. Eelgrass (Zostera marina) meadows provide essential coastal habitat and are vulnerable to a temperature‐sensitive wasting disease caused by the protist Labyrinthula zosterae. We assessed wasting disease sensitivity to warming temperatures across a 3500 km study range by combining long‐term satellite remote sensing of ocean temperature with field surveys from 32 meadows along the Pacific coast of North America in 2019. Between 11% and 99% of plants were infected in individual meadows, with up to 35% of plant tissue damaged. Disease prevalence was 3× higher in locations with warm temperature anomalies in summer, indicating that the risk of wasting disease will increase with climate warming throughout the geographic range for eelgrass. Large‐scale surveys were made possible for the first time by the Eelgrass Lesion Image Segmentation Application, an artificial intelligence (AI) system that quantifies eelgrass wasting disease 5000× faster and with comparable accuracy to a human expert. This study highlights the value of AI in marine biological observing specifically for detecting widespread climate‐driven disease outbreaks. [ABSTRACT FROM AUTHOR]

Background: Our objective was to determine the impacts of artificial intelligence (AI) on public health practice.Methods: We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically.Results: We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI's applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation.Conclusions: Experts are cautiously optimistic about AI's impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits. [ABSTRACT FROM AUTHOR]