Artificial intelligence and waveform diversity

The US Air Force (USAF) and US Department of Defense have a long history of research and development in the exploration of on-board sensors being used for detection of damage in aircraft structures. Initial activities can be traced to the early 1980’s which led to an extensive on-aircraft assessment of acoustic emission based (i.e. “passive”) sensor system. In the late 1990’s an effort was launched to revitalize the capability which cumulated in the “Hot Spots” program which explored the use of an ultrasonic guided wave (i.e. “active”) sensor system. Each of these programs encountered challenges that have hindered the use of these technologies on fixed-wing military aircraft. This paper briefly reviews these previous efforts, present current USAF Military Standards that define Structural Health Monitoring (SHM) for fixed wing aircraft, and provide a discussion of current and future concepts for research and development to resolve these challenges and enable eventual adaptation of SHM for fixed-wing applications. This will include a summary of current initiatives within the Materials and Manufacturing Directorate of AFRL and notional thoughts on potential projects for future developments required for this capability to be applied to fixed wing military aircraft.

ChemInformVolume 19, Issue 33 Physical Organic Chemistry ChemInform Abstract: Ionic Structure and Interactions in 1-Methyl-3-ethylimidazolium Chloride-AlCl3 Molten Salts K. M. DIETER, K. M. DIETER Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorC. J. JUN. DYMEK, C. J. JUN. DYMEK Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorN. E. HEIMER, N. E. HEIMER Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorJ. W. ROVANG, J. W. ROVANG Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorJ. S. WILKES, J. S. WILKES Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this author K. M. DIETER, K. M. DIETER Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorC. J. JUN. DYMEK, C. J. JUN. DYMEK Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorN. E. HEIMER, N. E. HEIMER Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorJ. W. ROVANG, J. W. ROVANG Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this authorJ. S. WILKES, J. S. WILKES Frank J. Seiler Res. Lab., US Air Force Acad., Colorado Springs, CO 80840-6528, USASearch for more papers by this author First published: August 16, 1988 https://doi.org/10.1002/chin.198833051Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume19, Issue33August 16, 1988 RelatedInformation

The Air Force Satellite Control Network (AFSCN) coordinates hundreds of satellite communication requests from various users every day. Building a conflict-free schedule from a large set of satellite communication requests is a difficult problem. Human schedulers are very adept at generating high-quality solutions, usually allowing all requests to be serviced. However, this process is time-intensive and requires highly trained, experienced individuals. That is, the teams of highly trained and experienced schedulers must manually check every schedule request received. Approximately half of all requests require adjustment to remove conflicts. The US Air Force (USAF) had an interest in further automating this process. Stottler Henke worked with the USAF to develop the Managed Intelligent Deconfliction and Scheduling (MIDAS) solution, an artificial intelligence (AI) tool for automatically scheduling satellite contacts, by incorporating the experience, insights, and expertise of human schedulers. MIDAS can now automate virtually all the scheduling of the satellite communication requests for the AFSCN, allowing schedulers to apply their expertise where it is really needed. MIDAS accomplishes this with a two-stage process that first shuffles tasks within their defined constraints before carefully applying a user-definable set of business rules that allow certain constraints to be relaxed when necessary. The system provides a familiar, user-friendly interface modeled on legacy Electronic Schedule Dissemination (ESD) systems to facilitate comparison and to allow users to switch from one interface to the other with relative ease. It runs on inexpensive consumer hardware and communicates with legacy systems via a well-defined plain-text file format: raw scheduling requests are imported to MIDAS, and scheduling results can be exported back to legacy tools. MIDAS now provides Air Force planning at a level not previously possible. A viable schedule can be assembled in a matter of minutes in order to assess the impact of possible outages, events, expansion of equipment, etc., that is, MIDAS provides the ability to perform "what-if" scenarios to assess the impact of a potential event or mission change. With only a few minutes of processing, MIDAS is able to deconflict all or virtually all communications requests for a given day. MIDAS has eliminated much of the repetitive work involved in scheduling and allows schedulers to focus on other important problems. This paper provides a history of MIDAS, an overview of its architecture and the many benefits MIDAS provides; in addition to its general applicability to non-USAF satellite communications scheduling.

Application of Systems Engineering to USAF Small Business Innovative Research (SBIR)

Artificial intelligence (AI) tools and techniques are undoubtedly being used in bioinformatics education, reflecting broader trends in education. However, many instructors and learners may be unaware of the full scope of potential uses for these tools within bioinformatics education, as well as effective practices for using them. Building on discussions held at the 6th Global Bioinformatics Education Summit, this perspective article provides insights about ways that AI might be used to generate or adapt instructional content, provide personalized help for learners, and automate assessment and grading. Additionally, we highlight AI skills that are important for bioinformatics learners to develop in order to effectively use AI as a bioinformatics learning tool. We highlight currently available tools in the quickly evolving AI landscape and suggest ways that instructors or learners might use such tools. Furthermore, we discuss key considerations and challenges associated with integrating AI into bioinformatics education, including ethical implications, potential biases, and the need to critically evaluate AI-generated content. Finally, we highlight the need for further research to better understand how AI tools are being used in practice and empower their effective and responsible use in bioinformatics education.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

Background Histological remission is an important target for Ulcerative Colitis (UC) treatment; however, scoring of histological images is time-consuming and prone to inter and intra-observer variability. Thus, a need exists for an accurate, reproducible, and reliable automated method. Previously, we demonstrated an Artificial Intelligence (AI) Tool using image processing and machine learning algorithms to measure histological disease activity using the Nancy index consistently and accurately.1 Here, we aim to enhance the capabilities of the AI Tool, by adding substantially more population-diversified training data while maintaining accuracy and robustness of results. Methods Eight global sites submitted 600 UC histological images. These were added to the 200 images previously used to train and validate the AI Tool. The 800-image dataset was divided into 2 groups: 90% used for training, 10% for testing. The novel AI algorithms were trained using state-of-the-art image processing and machine learning techniques based on deep learning and feature extraction. Cell and tissue regions of each training image were manually annotated, measured, and assigned a Nancy Index independently by 3 histopathologists, and used to further train the AI using over 43,000 characterisations. The AI Tool fully characterises histological images, identifying tissue types, cell types, cell numbers and locations, and automatically measures the Nancy Index for each image. Intra Class Correlation (ICC) and Confusion Matrix analyses were performed to evaluate the AI Tool and assess accuracy. Results The average ICC was 92.1% among the histopathologists and 91.1% between histopathologists and AI Tool, compared with 88.3% and 87.2% in the previous study.1 Confusion matrix analysis (Table 1) demonstrated the strongest correlation at the extremes of the Nancy Index, with 80% correlation between predicted and true labels for Nancy Scores of 0 or 4. When 2 adjacent scores were combined, correlations were stronger: 96% for a true Nancy score of 0 being predicted as 0 or 1, and 100% for a true Nancy score of 2 being predicted as 2 or 3. Conclusion By adding a larger number of images to the AI Tool training data, the robustness of the AI Tool was substantially improved while maintaining accuracy. The continued high correlation of AI Tool performance with the histopathologists reinforces the potential role for the AI Tool for IBD clinical applications. Fully characterising whole slides could standardise and validate an AI-driven scoring system for histology slides in IBD, eliminating the subjectivity of the human pathologist in assessment of disease activity.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

The integration of artificial intelligence (AI) tools in the workflow of scholarly writing, including scientific and medical writing, offers transformative advantages while raising critical ethical and practical concerns. Among the pros, AI substantially enhances efficiency by automating several time-consuming tasks such as literature review, data synthesis and text editing. In particular, tools such as ChatGPT, Grammarly, and SciSpace Copilot remarkably empower researchers when they draft, refine, and format manuscripts, increasing precision and speed. In addition, AI tools may foster inclusivity by assisting non-native English speakers with seamless translations and also enabling interdisciplinary collaboration, thereby hopefully democratizing access and boosting scientific communication and cooperation. However, the rapid adoption of novel AI tools brings significant challenges. First, there is a distinct risk of perpetuating biases in training datasets, and other key issues include ambiguity in authorship accountability and the potential erosion of critical thinking skills. In addition, AI tools could be purposefully misused to generate mock datasets and fraudulent papers (e.g. by paper mills), and this clearly poses a threat to academic integrity. This challenge is all too pressing given that traditional plagiarism detection tools often fall short against sophisticated AI-generated content. We hereby explore the pros and cons of AI on medical writing, poignantly leveraging Sergio Leone’s The Good, The Bad, and The Ugly movie as a metaphor. By providing general concepts as well as focusing on several key AI tools, we are hopeful this overview may prove useful to anyone wishing to conscientiously adopt AI tools for scientific writing. In addition, we make a compelling case for transparent guidelines, robust and freely available detection mechanisms, and ongoing critical oversight to ensure AI will be able to serve as a catalyst for innovation and dissemination without compromising the credibility of scholarly endeavors.

The paper presents the results of the research study conducted on students at the Faculty of Humanities and Social Sciences in Zagreb (FHSS), Croatia, at the end of 2024, focusing on their experiences with artificial intelligence (AI) tools, and its use in academic activities, students' trust in AI, and the future use of AI by students. While over 81 % of respondents reported using AI tools for study purposes, more than 89% did not receive any education about AI and expressed a need for such education. Generative AI tools for text creation are the most frequently used type of AI tools, but students still require additional knowledge to use them effectively. AI tools are also utilized for seeking advice, finding information, being available 24/7, and assisting in writing student papers, though not yet on a massive scale. Teachers remain the most trusted source for information and for recommending AI tools. Despite known shortcomings of AI tools such as content errors, potential costs and ethical or privacy concerns, students expressed their intention to continue using AI tools in the future.

The author discusses the use of IMPACTS (Integrated Manpower, Personnel, And Comprehensive Training & Safety), the new US Air Force acquisition approach that implements congressional law and US Department of Defense (DoD) manpower, personnel, training, and safety (MPTS) policy. Until now, the Air Force had no formal program that fully integrated these human-centered elements directly into the weapons system acquisition process (WSAP). The author focuses on the conceptual underpinnings of MPTS in system design, congressional and DoD MPTS direction, and the specific management structure of IMPACTS. He examines the effects that this new Air Force acquisition program may have on government acquisition and contractor engineering procedures and documentation. >

Background: Artificial intelligence (AI) tools have demonstrated success in US medical licensing examinations; however, their utility in infection prevention and control (IPC) remains unknown. Methods: The program of hospital epidemiology handles consultation calls and records each question and answer. Using 2022 data, we selected 31 frequently asked questions. We utilized four AI tools, including Chat GPT-3.5 and 4.0, Bing AI, and OpenEvidence, to generate answers. We predefined scales (Table 1) to capture responses by three reviewers, including two hospital epidemiologists and one infection preventionist. The mean score of ≥ 3 and ≥ 4 was considered acceptable in accuracy and completeness, respectively. We reported the percentage of responses with acceptable accuracy and completeness out of assessed questions for each category. Results: Among 31 questions, 16 were associated with isolation duration, 9 with healthcare personnel (HCP) exposure, 4 with cleaning contaminated rooms, and 2 with patient exposure. Regarding accuracy, most AI tools performed worse in questions about isolation duration, ranging between 75% and 93.8%. All AI tools, except OpenEvidence, had a 100% accuracy rate for HCP and patient exposure. All AI tools had a 100% accuracy rate for contaminated room handling. The highest overall acceptable accuracy rate was observed in Chat GPT-3.5. Regarding completeness, most AI tools performed worse in questions about isolation duration, ranging between 44% and 75%. All AI tools, except OpenEvidence, had a 100% completeness rate for contaminated rooms and patient exposure. The highest overall acceptable completeness rate was observed in Bing AI (Table 2). Conclusions: All AI tools provided reasonable answers to commonly asked IPC-related questions, although, there were variations among different tools used. AI could be used to supplement the infection control program, especially if resources are limited.

Technical English (TE) proficiency is crucial for the future careers of polytechnic students. While Artificial Intelligence (AI) tools offer significant potential to enhance language learning, their effectiveness relies on student acceptance and use. There is limited understanding of what drives polytechnic students to adopt these tools specifically for TE. This study aims to identify the key factors influencing polytechnic students' acceptance and use of AI tools in this context and employ a quantitative approach based on the Technology Acceptance Model (TAM) and Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze survey data collected from 100 Polytechnic Kota Bharu (PKB) students enrolled in TE courses. The research investigates core antecedents, primarily perceived usefulness (PU) and perceived ease of use (PEOU), and their impact on students' behavioral intention (BI) to use AI tools. The potential influence of external factors such as social influence and lecturer support are examined. The study found PEOU was identified as a critical antecedent, which significantly positively affected PU and BI. The study reaffirmed the significant predictive power of BI on AU, indicating that students' stated intentions reliably translate into their subsequent usage behaviour. This research will offer practical recommendations for educators seeking to integrate AI tools effectively into TE instruction. Theoretically, this study contributes to understanding technology adoption within the specific domain of technical and vocational language education, providing valuable insights for leveraging AI to improve essential communication skills for aspiring technical professionals.

This study aims to explore English Language Education Study Program (ELESP) students' perceptions of the use of artificial intelligence (AI) tools in supporting their academic writing processes and identify the benefits and limitations they encounter when using these tools in their writing process. Six sixth-semester students from the ELESP were purposively selected based on their prior engagement with AI tools such as ChatGPT, Grammarly, or QuillBot in academic writing tasks. Data were collected through semi-structured, in-depth interviews that allowed participants to express their experiences openly while giving the researcher flexibility to probe further when necessary. Thematic analysis was used to analyze the findings. The findings indicate that the majority of students perceive AI paraphrasing tools as highly useful in the writing process, both in the linguistic and affective dimensions. The majority of EFL students also perceive AI tools as providing substantial benefits in supporting academic writing. Given these insights, it is suggested that educators and institutions integrate AI tools into writing pedagogy while providing explicit guidance on ethical and critical use.

WHAT WITH delivering thousands of babies a year, caring for family members with physical and mental disabilities, rehabilitating adults with alcohol and/or drug abuse problems, counseling couples with marital woes, and dealing with the various ailments and injuries that can afflict a basically healthy young population, it's cause for wonder that peacetime personnel at the US Army and US Air Force medical centers in West Germany are able effectively to carry out the other half of their mission: being prepared to provide medical support in the event of war. Visits to the two centers under the Heidelberg-based Seventh Medical Command, Frankfurt Army Regional Medical Center (FARMC) and Landstuhl Army Regional Medical Center (LARMC), and to the Wiesbaden Air Force Regional Medical Center give some idea how they do it. The task is clearly immense. All three are responsible for primary and tertiary care in their own approximately 13 000 km