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Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported. • The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology to investigate the feasibility of incorporating digital cytology and artificial intelligence applications into the workflow of the laboratory. • The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and artificial intelligence (AI) with multiple industry corporate representatives. • Part 2 of the white paper presented here provides a comprehensive review of artificial intelligence in cytology practice along with best practice recommendations and legal considerations. [ABSTRACT FROM AUTHOR]

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation. [ABSTRACT FROM AUTHOR]

Radiography education is pivotal in training skilled radiographers for diagnostic imaging and therapeutic applications. With technological advancements, interest in innovative educational tools to enhance traditional teaching methods is growing. This discussion paper explores the possibility of the integration of ChatGPT, a cutting-edge conversational AI language model, into radiography education. We report that ChatGPT offers interactive learning opportunities that can facilitate learning. It also provides self-paced learning, revision platforms, and supports educators in scenario creation, assessment development, group collaboration, and professional and research activities. Despite these benefits, it is important to carefully consider issues related to academic integrity and privacy, along with the opportunities and challenges presented by this new technology in radiography education. This paper highlights some of the prospects and limitations of the potential applications of ChatGPT in radiography education, underscoring the benefits for both students and educators. However, its implementation must be considered thoughtfully and ethically, taking into account its strengths and limitations. Integrating ChatGPT in radiography education has the potential to improve radiography education by improving digital literacy and graduate outcomes of students while streamlining the preparation process for educators. However, ethical implementation is vital for optimal outcomes. [ABSTRACT FROM AUTHOR]

Recently, I was at a Microsoft Technology Center to discuss the augmented reality (AR) technology developed in my lab for treatment of post-traumatic stress disorder (PTSD). We also explored the possibility of using artificial intelligence (AI) to automate interactions with digital AR characters. We were also given a tour of the Microsoft 365 Copilot AI technology. This experience, along with my upcoming experiences with the rapidly evolving and publicly available AI technology, triggered a series of questions and reflections about the use of AI in scientific work and writing. In this article, the author reflects upon recent advancements in artificial intelligence (AI) technologies have led to profound discussions about AI's role in scientific research and education. AI technologies are now capable of summarizing and analyzing large volumes of data, creating presentations, and even drafting parts of scientific papers and grants with minimal human input. With their exponential ongoing advancements, they will become even more capable in doing these tasks. As boundaries between humans and AI blurs, serious questions arise about the future roles, responsibilities, and identity of academic researchers, intellectual property, publishing and grantsmanship, and equity in the world of AI potentiated research. [ABSTRACT FROM AUTHOR]

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. Discussion paper. We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association – International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association – International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking. [ABSTRACT FROM AUTHOR]

We study a five-species cyclic system wherein individuals of one species strategically adapt their movements to enhance their performance in the spatial rock–paper–scissors game. Environmental cues enable the awareness of the presence of organisms targeted for elimination in the cyclic game. If the local density of target organisms is sufficiently high, individuals move towards concentrated areas for direct attack; otherwise, they employ an ambush tactic, maximising the chances of success by targeting regions likely to be dominated by opponents. Running stochastic simulations, we discover that the ambush strategy enhances the likelihood of individual success compared to direct attacks alone, leading to uneven spatial patterns characterised by spiral waves. We compute the autocorrelation function and measure how the ambush tactic unbalances the organisms' spatial organisation by calculating the characteristic length scale of typical spatial domains of each species. We demonstrate that the threshold for local species density influences the ambush strategy's effectiveness, while the neighbourhood perception range significantly impacts decision-making accuracy. The outcomes show that long-range perception improves performance by over 60%, although there is potential interference in decision-making under high attack triggers. Understanding how organisms' adaptation their environment enhances their performance may be helpful not only for ecologists, but also for data scientists, aiming to improve artificial intelligence systems. • Stochastic simulations of the rock–paper–scissors model with Ambush strategy are performed. • The changes in the spatial patterns provoked by the behavioural strategy are quantified. • Organisms' performance in the rock–paper–scissor game is computed. • The impact of the organisms' perception radius in the accuracy of the strategy is investigated. [ABSTRACT FROM AUTHOR]

In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

ChatGPT could allow students to plagiarize the content of their coursework with little risk of detection. Little is known about undergraduate willingness to use AI tools. In this study, psychology undergraduates (N = 160) from the United Kingdom, indicated their willingness to use, and history of using, ChatGPT to write university assignments. Almost a third (32%) indicated that they would use such tools; 15% indicated that they had used them already. Neither personality (conscientiousness, agreeableness, Machiavellianism, narcissism), academic performance, nor study skills self-efficacy could predict future use of AI tools. A novel Degree Apathy Scale was the only significant predictor. Willingness to use AI tools was greater when the risk of getting caught was low, and punishment was light, particularly for those high in degree apathy. Findings suggest that degree apathy is a key risk factor in academic misconduct. Wider research and pedagogical applications of degree apathy are discussed. • Thirty-two percent of students surveyed reported that they would use ChatGPT on future academic assignments. • Future use of AI tools was significantly predicted by a new Degree Apathy Scale. • Big Five and Dark Triad scores were not significant predictors, nor was previous academic performance. • Likelihood of cheating using ChatGPT was reduced with increasing risk of detection or severity of punishment. [ABSTRACT FROM AUTHOR]

Emotional intelligence (EI) involves recognizing, regulating, and understanding emotions in oneself and others, enabling effective relationship management. Emotional Intelligence acts as a differentiator in both personal and professional lives. Nevertheless, many people lack the ability of conveying and interpreting human emotions efficiently due to which they fall short in their social and professional lives. However, this can be improved if people know their Emotional Quotient (EQ) score and work towards improving it by implementing certain skills. Although traditional methods for measuring EQ exist, majority of them are manual and are built on self-efficacy based questionnaire which are often susceptible to faking. In order to overcome these lacuna, this paper proposes a digital approach that can be used for an automatic evaluation of EQ of a person via audio visual signal input. The paper devises four major factors self-awareness, self-regulation, self-motivation and social relationships with their weightages that can be utilised for automatic evaluation through the use of technology. The paper also proposes quantifiable parameters for measuring these factors. The proposed criteria were developed with the emergence of deep learning and its applicability in assessing a person's EQ in mind. [ABSTRACT FROM AUTHOR]

[Display omitted] • We examine how the introduction of AI affects enterprise environmental performance. • The application of AI can reduce the pollution emission level of enterprises. • Improving labor substitution can improve enterprise environmental performance. • Improving technological innovation can improve firm environmental performance. • The use of AI has a different impact on different enterprises. The progress of artificial intelligence (AI) technology is an important way to solve the problem of global sustainable development. This paper discusses how AI, as a new scientific force, affects the pollution reduction of enterprises from a micro perspective. In this paper, the robot data provided by the International Federation of Robotics and pollution data of industrial enterprises in China are used for empirical analysis to test the effect of AI on pollution performance of enterprises. The results show that the application of AI can reduce enterprise emissions through technological innovation effect and labor substitution effect, and this result remains robust after sample substitution and variable substitution. Further heterogeneity analysis shows that AI has a more significant impact on enterprises in big cities and southern cities, as well as large enterprises, old enterprises and non-technology-intensive enterprises. The research results of this paper provide an effective reference for solving how to achieve green and sustainable development of artificial intelligence applications. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) tools to aid in summarizing information in medicine and research has recently garnered a huge amount of interest. While tools such as ChatGPT produce convincing and naturally sounding output, the answers are sometimes incorrect. Some of these drawbacks, it is hoped, can be avoided by using programmes trained for a more specific scope. In this study we compared the performance of a new AI tool (the-literature.com) to the latest version OpenAI's ChatGPT (GPT-4) in summarizing topics that the authors have significantly contributed to. The AI tools were asked to produce a literature review on 7 topics. These were selected based on the research topics that the authors were intimately familiar with and have contributed to through their own publications. The output produced by the AI tools were graded on a 1–5 Likert scale for accuracy, comprehensiveness, and relevance by two fellowship trained consultant radiologists. The-literature.com produced 3 excellent summaries, 3 very poor summaries not relevant to the prompt, and one summary, which was relevant but did not include all relevant papers. All of the summaries produced by GPT-4 were relevant, but fewer relevant papers were identified. The average Likert rating was for the-literature was 2.88 and 3.86 for GPT-4. There was good agreement between the ratings of both radiologists (ICC = 0.883). Summaries produced by AI in its current state require careful human validation. GPT-4 on average provides higher quality summaries. Neither tool can reliably identify all relevant publications. • Literature review is a crucial, but labour intensive, part of research. • Artificial intelligence tools have the potential to realise significant time savings. • AI driven tools can in certain cases produce impressive literature reviews. • However, current AI literature review tools are too unreliable for routine use. [ABSTRACT FROM AUTHOR]

This paper is the first of a Series on theranostics that summarises the current landscape of the radiopharmaceutical sciences as they pertain to oncology. In this Series paper, we describe exciting developments in radiochemistry and the production of radionuclides, the development and translation of theranostics, and the application of artificial intelligence to our field. These developments are catalysing growth in the use of radiopharmaceuticals to the benefit of patients worldwide. We also highlight some of the key issues to be addressed in the coming years to realise the full potential of radiopharmaceuticals to treat cancer. [ABSTRACT FROM AUTHOR]

Although radiotherapy continues to evolve as a mainstay of the oncological armamentarium, research and innovation in radiotherapy in low-income and middle-income countries (LMICs) faces challenges. This third Series paper examines the current state of LMIC radiotherapy research and provides new data from a 2022 survey undertaken by the International Atomic Energy Agency and new data on funding. In the context of LMIC-related challenges and impediments, we explore several developments and advances—such as deep phenotyping, real-time targeting, and artificial intelligence—to flag specific opportunities with applicability and relevance for resource-constrained settings. Given the pressing nature of cancer in LMICs, we also highlight some best practices and address the broader need to develop the research workforce of the future. This Series paper thereby serves as a resource for radiation professionals. [ABSTRACT FROM AUTHOR]

[Display omitted] • 2D MXenes are the key materials in novel flexible electronic devices. • MXene-based electronics on various substrates (hydrogel, paper, etc.) are reviewed. • MXene-based electronics can be integrated with artificial intelligence. • Applications of MXenes in the new generation of wearable devices are summarized. • Challenges and difficulties in designing MXene-based electronics are discussed. • Future research directions are also suggested for wearable MXene electronics. Wearable electronic devices find increasing applications in mobile medical sensing and monitoring, human–computer interaction, and portable energy collection and storage, owing to their flexibility and stretchability. In recent years, transition metal nitrides and carbides, known as MXenes, have become cornerstones for the preparation of novel flexible electronic devices because of their excellent electrical conductivities, abundant surface functional groups, and large specific surface areas. This review presents the latest developments in MXene-based wearable electronic products, including hydrogels, paper, composite materials, and self-powered devices. These products can be integrated with artificial intelligence to show unique applications in healthcare, inspection, and control of human-like machines. The application prospects of MXenes in the new generation of wearable electronic devices are forecast, the challenges and difficulties in designing MXene-based wearable electronic devices are discussed, and corresponding solutions are suggested. This review also provides future research directions for the development of MXenes for pliable and wearable applications. [ABSTRACT FROM AUTHOR]

In an age where technology is evolving at a sometimes incomprehensibly rapid pace, the liver community must adjust and learn to embrace breakthroughs with an open mind in order to benefit from potentially transformative influences on our science and practice. The Journal of Hepatology has responded to novel developments in artificial intelligence (AI) by recruiting experts in the field to serve on the Editorial Board. Publications introducing novel AI technology are no longer uncommon in our journal and are among the most highly debated and possibly practice-changing papers across a broad range of scientific disciplines, united by their focus on liver disease. As AI is rapidly evolving, this expert paper will focus on educating our readership on large language models and their possible impact on our research practice and clinical outlook, outlining both challenges and opportunities in the field. "To improve is to change; to be perfect is to change often." ― Winston S. Churchill [ABSTRACT FROM AUTHOR]

Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered and implemented. The state-of-the-art in application of various optimisation and artificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP. [ABSTRACT FROM AUTHOR]

• Artificial intelligence can facilitate inclusive approach to heritage planning. • This paper reveals the cultural significance attached to the heritage property. • Artificial intelligence can contribute to value-based heritage planning. Experts have always played an important role in heritage planning, practice, and theory. There is a wealth of literature published every year regarding heritage and its cultural significance. Experts also contribute to heritage planning and developing policy documents. Still, literature is rarely used as a source of primary research to systematically reveal and compare experts' opinions on the cultural significance of built heritage. Analyzing them as a whole is costly and time-consuming, especially on built heritage, when much has been written about. While the automation of methods has proven to mitigate such restrictions in other fields, as digital humanities, their application in heritage planning, practice, and theory is still scarce. Hence, this paper aims to investigate the potentials of AI models (e.g., multi label text classification) in analyzing scientific documents, revealing the cultural significance of built heritage, values and attributes. This was done to better understand the similarities and differences between the experts' opinions. Yazd, Iran, is taken a case study, with a particular focus on windcatchers, a key attribute conveying cultural significance, of outstanding universal value, due to its inscription on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the application of AI in heritage planning; 2) methodology of data collection and data analysis related to coding values and attributes of windcatchers, addressed in relevant documents; 3) preliminary findings on the experts' opinions over values and attributes of windcatchers. Results contribute to the scientific discussion, revealing the cultural significance of windcatchers of Yazd from experts' point of view. Besides, the potential of AI for heritage planning is revealed in terms of (de)coding and measuring the cultural significance of built heritage from the available documents, showing the opinions of experts with various backgrounds. This model can be applied to other key attributes in Yazd and other case studies and scales to support heritage planning, practice, and theory. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

The evolution of advanced technology across various industries has provided opportunities for improvement in how professionals carry out their jobs. Artificial Intelligence (AI) is a rapidly evolving technology that brings great convenience to life, but at the same time, some risks need to be assessed. This technical paper will discuss AI processes that are taking place in the current scenario at accounting firms and their impact on them. We discussed AI's benefits to the Accounting and Auditing profession and its employees. This paper discussed AI's risks and challenges to Accounting firms and their employees. [ABSTRACT FROM AUTHOR]

Emotion AI deals with the sentiments of human beings at various domains. It is named emotion AI as using the capabilities of AI the emotions of humans would be interpreted and analysed. The objective of this paper is to learn from the experience of patients towards the healthcare facilities by studying and analysing the sentiments of the patients using machine learning. This paper focus on training the machine for reading the reviews given by patients who have used various healthcare facilities. The machine will be trained to understand the polarity for each healthcare facility in terms of cleanliness, availability of doctors, interaction of doctors with patients etc. The code is implemented in python with various libraries required for machine Learning. The code is able to extract the polarity and is able to handle the emotions of the patients for questions answered in the dataset by patients. The paper would contribute and help the patients decides which healthcare facility they shall choose based on the experiences of various other patients. For this the goodness score for every healthcare facility is calculated and implemented using Machine learning. It's the contribution of artificial Intelligence and machine learning for healthcare Domain. [ABSTRACT FROM AUTHOR]

Diabetes mellitus remains a global health challenge, requiring innovative solutions for effective disease management. This paper offers a thorough analysis of diabetes technologies, highlighting their various roles in diabetes care. Through a thorough review of the literature and analysis of emerging trends, we explore the multifaceted impact of technology on diabetes care. We investigate the key role of continuous glucose monitoring systems, insulin pumps and smart insulin pens in achieving optimal glycaemic control. The paper also evaluates the integration of artificial intelligence and machine learning algorithms in predictive modelling for early detection of glucose fluctuations, ultimately preventing diabetes-related complications. Additionally, studies the potential of telemedicine and mobile applications in enhancing patient engagement and self-management. Moreover, the review covers advancements in closed-loop insulin delivery systems, offering insights into their clinical effectiveness and potential to revolutionize diabetes care. Ethical and privacy considerations related to the use of patient data in these technologies are discussed, emphasizing the importance of striking a balance between technological innovation and patient security. This paper's evidence synthesis underscores the increasing influence of diabetes technologies on patient outcomes, quality of life, and healthcare systems. It underscores the need for multidisciplinary collaboration between healthcare professionals, researchers and technology developers to ensure the seamless integration and accessibility of these tools to patients living with diabetes. This study serves as a valuable resource for clinicians, researchers, and policymakers, providing a comprehensive view of evolving diabetes technologies and their potential in the field. [ABSTRACT FROM AUTHOR]

The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions. [ABSTRACT FROM AUTHOR]

Currently, the public security sector is faced with an increasing administrative burden that limits the ability of police officers to focus on core security tasks. This paper focuses on the possibility of using large-scale language models (LSMs) as an innovative tool to address this challenge. Based on a careful literature review and analysis of current trends in artificial intelligence, the author team develops a concept for integrating GPTs into police practice, with an emphasis on the potential for reducing administrative burden and supporting efficient processing of relevant information. As part of this research, we have identified key areas of policing where AI could bring significant value, including data analysis and document production assistance. However, it should be emphasized that this technology is still in its early stages of development and its implementation would require a carefully considered approach involving interdisciplinary collaboration and further research to test the theoretical assumptions presented in this study. Thus, this paper contributes to a deeper understanding of the potential benefits and challenges of integrating GPT into policing practice and outlines a path towards future innovative solutions in the field of public safety. [ABSTRACT FROM AUTHOR]

The increasing exploitation of Artificial Intelligence (AI) technologies has enabled the design of user interfaces in a way that integrating artificial intelligence capabilities has become crucial in the modern digital landscape. Exploring the main features and best practices for designing user interfaces for Web applications, which effectively support and leverage AI functionalities, is currently one of the relevant topics in this context. This research work discusses the fundamental principles of user interface (UI) design, and the challenges posed by the integration of AI into web applications. It emphasizes the need to strike a balance between the AI advanced capabilities and the users' ability to understand and control the system. Furthermore, the paper highlights the importance of creating intuitive and engaging UI designs that empower users to interact with AI-driven features effortlessly. The study presents a comprehensive analysis of various UI design techniques specifically tailored for AI-enabled web applications user interfaces. Additionally, the paper explores the incorporation of AI-driven recommendation systems, personalized interfaces, and adaptive designs, which dynamically adapt to users' preferences and behavior. To validate the proposed user interface design principles, the study presents a proposal for a guidelines structure that promotes empirical evaluations through user studies and usability testing. Results collected via a survey based on measuring the effectiveness and user satisfaction of AI-enabled Web interfaces. User interfaces in real-life scenarios are presented and provides information on the impact of UI design decisions on user interaction and overall experience. The outcomes of this research work contribute to a deeper understanding of UI design for AI-supported Web applications user interfaces and offer practical guidelines for designers and developers. By embracing the suggested principles, organizations and designers can create Web interfaces that effectively harness the power of AI while prioritizing user-centricity, accessibility, and ethical considerations. [ABSTRACT FROM AUTHOR]

The advent of artificial intelligence (AI) technology has ushered in an era of intelligent education, catalyzing profound changes in teaching methods and reshaping the roles of educators. This paper explores the intricate relationship between technology, education, and teachers by drawing upon relevant theories from the field of technological sociology, particularly the concept of "one skill for one person." It scrutinizes the evolving role of teachers in the context of artificial intelligence, delving into the mechanisms that underpin their functions in this rapidly evolving educational landscape. Through a historical examination of educational technology innovation, this paper delineates a three-tiered transformation in the relationship between artificial intelligence and university teachers, spanning from "technology exclusion" to "technology complementarity" and finally to "technology dependence." The paper used a mixed-methods strategy, combining qualitative and quantitative approaches. These phases illustrate the evolving synergy between human educators and AI, ultimately contributing to the modernization of the educational system and the enhancement of educational capabilities. This research sheds light on how the integration of AI can enhance the educational experience and empower educators, ensuring the continued advancement of education in the digital age. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) applications within the medical field have showcased promising potential in disease identification and diagnosis. This study presents a comprehensive analysis of cutting-edge AI-based methods for the identification of Parkinson's disease. This paper encompasses extensive testing on substantial datasets containing both PD patients and healthy individuals, aiming to assess the efficacy of AI models. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) are employed to evaluate model performance rigorously. This paper is not only reviewing the state-of-the-art AI techniques for Parkinson's disease identification but also introduces novel contributions in the field. Our results demonstrate the potential of AI in achieving early and accurate PD diagnosis, offering hope for improved patient care and treatment outcomes. Furthermore, this study paves the way for future research and development in the domain of AI-driven healthcare, with the ultimate goal of enhancing the quality of life for individuals suffering from Parkinson's disease and similar neurodegenerative conditions. [ABSTRACT FROM AUTHOR]

This paper summarizes the effective components of computer-aided design and computer-aided manufacturing (CAD-CAM) resin composites that contribute to achieving greater mechanical properties and further development. In silico multi-scale analysis, in silico nonlinear dynamic finite element analysis (FEA), and artificial intelligence (AI) were used to explore the effective components of CAD-CAM resin composites. The effects of the filler diameter and silane coupling ratio on the mechanical properties of CAD-CAM resin composites have been clarified through multi-scale analysis. The effects of the filler contents, and filler and monomer compositions have been investigated by AI algorithms. The fracture behavior of CAD-CAM composite crown was analyzed using in silico non-linear dynamic FEA. The longevity of CAD-CAM composite crown was assessed through step-stress accelerating life testing (SSALT). As the filler diameter decreases, there is an increase in elastic moduli and compressive strengths at the macroscale. At the nanoscale, a decrease in the filler diameter results in a decrease in the maximum value of the maximum principal strain. When the silane coupling ratio decreases, there is a decrease in the elastic modulus and compressive strength. According to the exhaustive search and feature importance analysis based on the AI algorithm, the combination of certain components was narrowed down to achieve a flexural strength of 269.5 MPa. The in silico non-linear FEA successfully detected the sign of the initial crack of the CAD-CAM composite molar crown. The SSALT revealed that CAD-CAM resin composite molar crowns containing nanofillers with a high fraction of resin matrix demonstrated great longevity. This paper summarized the effective components of CAD-CAM resin composites for their further development. The integration of in vitro and in silico approaches will expedite the advancement of CAD-CAM resin composites, offering benefits such as time efficiency and reduction of material waste for researchers and manufacturers. • The effective components of CAD-CAM resin composites (RC) have been summarized. • A smaller filler and a high silane coupling ratio enhance the mechanical properties of CAD-CAM RC. • Non-linear FEA has detected the sign of the initial crack in the CAD-CAM composite crown. • AI has been utilized to narrow down the effective components that achieve high flexural strength. • CAD-CAM composite crowns with a high fraction of resin matrix demonstrate remarkable longevity. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic condition that is characterized by high blood glucose levels and can cause damage to multiple organs over time. Continuous monitoring of glucose levels is essential for both diabetic and non-diabetic individuals. There have been major developments in glucose monitoring technology over the past decade, which have been driven by research and industry efforts. Despite these significant advancements, the area of glucose biosensors still faces significant challenges. This paper presents a comprehensive summary of the latest glucose monitoring technologies, including invasive, minimally invasive, and non-invasive methods. Subsequently, we bring together the electronic components, wireless communication technologies, and energy harvesting opportunities, along with the limitations and challenges associated with current glucose monitoring solutions. This is followed by highlighting the potential integration of health records generated by continuous glucose monitors and artificial intelligence (AI) techniques to define precise diabetes management protocols. This integration achieves accurate results with constrained prediction horizons employing a time series of continuous glucose readings. The paper emphasizes the need for further advancements in glucose monitoring technology to improve diabetes management and address the critical need in clinical practice for improved glucose monitoring technologies with translational implications. • Comprehensive exploration of diverse glucose sensing technologies and their underlying principles. • Highlighting the crucial role of wearable biosensors in monitoring bio-molecules. • Detailed overview of essential electronic components for continuous glucose monitoring (CGM) systems. • Discussion on energy storage options, including self-powered solutions and advancements in battery technologies. • Emphasis on challenges in CGM technology, integration of AI, commercial solutions analysis, and future directions. [ABSTRACT FROM AUTHOR]

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers' attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions. • New controller designed by combining adaptive terminal synergetic and integral backstepping techniques. • Reference voltage generation using machine learning regression algorithm. • Estimation of unmeasured variables through Neural Network. • Improved particle swarm optimization method to determine the design parameters. • Comparison of proposed controller with others under real climatic conditions. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating machinery's safety and effective operation. The success of most current methods relies on the assumption that sufficient high-quality labeled datasets can be obtained for model training. However, in real-world industrial scenarios, obtaining such datasets is difficult or nearly impossible, thereby hindering the practical implementation of these methods. The integration of virtual modeling and transfer learning offers a powerful approach to meet the above challenge. Abundant virtual data of different fault categories can be acquired in the virtual space with highly flexible and at a low cost, and transfer learning can enhance the practical utility of these virtual data for contributing to the construction of diagnosis models. Therefore, this paper proposes a digital twin-driven partial domain fault diagnosis method based on unlabeled physical data and labeled virtual data. First, a virtual model of rotating machinery is built to generate labeled virtual fault data with enough fault types. Then, an adversarial transfer learning network is developed to leverage the effective knowledge from the virtual and physical data. Meanwhile, a weighting learning module is introduced to reduce the negative effect caused by the redundant fault categories in the virtual space. Finally, the proposed digital twin-driven transfer learning network is trained with the labeled virtual data and unlabeled physical data. Experiments on a light truck transmission system demonstrate that the proposed method achieves satisfactory diagnostic performance even without labeled physical fault data, contributing to the advancement of AI engineering applications. [ABSTRACT FROM AUTHOR]

The factors contributing to different levels of artificial intelligence (AI) adoption by librarians and their patrons need clarifying in the context of literature reviews. This paper addresses this need by exploring the transformative impact of AI on literature reviews, particularly within academic librarianship in the health sciences. Drawing on literature and professional experience, it examines how AI is reshaping reviews, potentially extending their meaning beyond text-based sources to accommodate multimedia content and predictive insights. While highlighting AI's promise in enhancing research efficiency and comprehensiveness, the paper also notes the lack of documentation of AI's uptake for literature reviews, perhaps reflecting concerns over reliability and biases. Proposed strategies for moving forward include matching different literature reviews with the most appropriate AI systems. This alignment guides librarians and researchers in navigating the complexities of AI adoption, using human oversight to ensure the integrity and quality of AI content. The paper underscores the importance of education, training, and continuous consultation to promote trustworthy and responsible AI utilization. This pathway foresees more robust outcomes from literature reviews in domains like health care in the digital age. [ABSTRACT FROM AUTHOR]

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions requiring speed and accuracy. Machine learning and artificial intelligence have become critical in supporting extensive data collection and real-time processing to assist decision-makers in configuring scarce resources. In this paper, we present four different applications that investigate issues related to the broader maritime supply chain security domain affecting the planning, execution, and performance of complex systems while exploring novel frontiers in cyber research and education. This paper will focus on Machine Learning and AI applications on Unmanned Aerial Systems and Cryptography related to Cybersecurity in Maritimes and Shipbuilding Spheres. [ABSTRACT FROM AUTHOR]

The rapid advancement of robotics and artificial intelligence paves the way for collaborative robotics to revolutionise industrial operations. Collaborative robotics can bring significant improvements in productivity, efficiency, and safety. Recently, the concept of Symbiotic Human-Robot Collaboration (SHRC) has emerged within the field of collaborative robotics, emphasising the seamless and adaptive integration of human and robotic capabilities within a shared workspace to optimise overall production performance. However, existing collaborative systems frequently face difficulties in establishing dynamic relationships with humans and adapting to evolving conditions, ultimately restricting their overall potential. This paper investigates the essential features required for collaborative workstations to enable SHRC. Moreover, with the aim of fully harnessing the power of collaborative robotics, the paper introduces a design methodology that supports the development of symbiotic workstations for specific assembly operations. A real-world case study is provided, demonstrating the practical implementation and benefits of the proposed design approach. [ABSTRACT FROM AUTHOR]

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing. [ABSTRACT FROM AUTHOR]

To satisfy the rising demand for higher product quality and giga-casting requirements, the casting process is undergoing significant changes. However, current control methods rely significantly on human expertise and experience, making process availability and stability difficult to ensure. The semisolid casting process is more complicated than conventional liquid casting due to the additional casting parameters incorporated during the slurry preparation, which can have an effect on the quality of the final product. Therefore, an efficient tool is required to simplify the complete process of semisolid casting. The introduction of an AI system to aid in the supervision of the casting manufacturing procedure is one potential solution. This paper introduces a new casting system named "Smart-Cast" developed for this specific purpose. The paper describes the functions of the system and its current development process. Using an AI system as an assistant can help to achieve the goal of enhancing the efficacy of casting process control, and it can also help foundries step into the Industry 4.0 era. [ABSTRACT FROM AUTHOR]

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research. [ABSTRACT FROM AUTHOR]

Industrial maintenance plays a crucial role in manufacturing by significantly reducing machine failure time and minimizing costs, especially in the revolution of Industry 4.0. Consequently, researchers and industrial engineers have continuously focused on this area. Manufacturing companies possess extensive maintenance reports or logs containing valuable textual information, which offers a new avenue for exploring effective industrial maintenance methods. Natural Language Processing (NLP), a subfield of Artificial Intelligence, has demonstrated remarkable potential in analyzing maintenance reports and achieving promising results in various tasks. This paper presents a comprehensive systematic literature review that specifically concentrates on the applications of NLP approaches employed in the field of industrial maintenance. Additionally, this review analyzed the datasets utilized in previous studies and the evaluation measures adopted, which can serve as a valuable resource for other researchers seeking potential solutions in maintenance. Furthermore, the paper discusses the challenges encountered in applying NLP to industrial maintenance and outlines future research directions in this domain. By conducting this systematic literature review, we provide a comprehensive understanding of the current state of NLP applications in industrial maintenance, identify gaps in the existing literature, and guide future research efforts in leveraging NLP techniques for enhanced maintenance practices. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications. [ABSTRACT FROM AUTHOR]

The human-centered view emphasizes various aspects including usability, acceptance, and understandability, as they emerge in the interaction between humans and intelligent systems. For intelligent assistance systems to provide long-term added value in working environments, it is of major importance to develop a positive user experience. However, a positive user experience does not appear by itself but must be systematically investigated and designed. In this paper, we focus on the role of operator 5.0 in manufacturing and provide an overview of the main challenges and potentials related to working with intelligent systems. Drawing upon state-of-the-art literature and findings from industry (context and expert interviews and user experience workshops), this paper investigates the main discontinuous production processes (i.e., process monitoring, process optimization, process maintenance) that will undergo a series of changes due to integration of cutting-edge technologies (such as Digital Twins, Extended Reality, and Artificial Intelligence). It then identifies requirements and discusses key challenges for the realization of human-centered assistance systems. By highlighting underexplored research questions and potential improvements in industrial settings, this paper provides researchers and practitioners to establish a holistic understanding of the user experience along the industry 5.0 journey [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, robots are widely used in various fields, grasping detection has been the focus of intelligent robot research. A dual manipulator grasping detection model based on Markov decision process is proposed to realize the stable grasping with complex multiple objects in this paper. Based on the principle of Markov decision process, the cross entropy convolutional neural network and full convolutional neural network are used to parameterize the grasping detection model of dual manipulators which are two-finger manipulator and vacuum sucker manipulator for multi-objective unknown objects. The data set generated in the simulated environment is used to train the two grasping detection networks. By comparing the grasping quality of the detection network output the best grasping by the two grasping methods, the network with better detection effect corresponding to the two grasping methods of two-finger and vacuum sucker is determined, and the dual manipulator grasping detection model is constructed in this paper. Robot grasping experiments are carried out, and the experimental results show that the proposed dual manipulator grasping detection method achieves 90.6% success rate, which is much higher than the other groups of experiments. The feasibility and superiority of the dual manipulator grasping detection method based on Markov decision process are verified. [ABSTRACT FROM AUTHOR]

In recent years, social networks have changed the way people communicate, while the dissemination and obtaining of information has become fast and convenient. However, it is a double-edged sword, providing both a breeding ground for disinformation and a medium for its rapid dissemination. Meanwhile, falsehoods are proliferating and becoming a global risk, and deepfake technology makes it harder to discern. Therefore, this paper proposed a large language model-based algorithm to detect disinformation with generated videos and photos, which enhances the ability to discriminate fake news by implanting alignment and task specific instructions on the Llama model. Eventually, the result indicated that the proposed method has the potential to achieve superior performance and aligned with people's judgment of things. Furthermore, the paper delves into practical discussions concerning fine-tuning the model within the constraints of limited computational power, highlighting the challenges and potential solutions in optimizing the algorithm for applications. [ABSTRACT FROM AUTHOR]

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach. [ABSTRACT FROM AUTHOR]

Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1 % range-normalised and 1 deg, respectively, an established rule of thumb for the navigation measurement accuracy during final approach. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure. • A novel AI-based solution for spacecraft docking and refuelling is presented. • First demonstration of deep learning for vision-based pose estimation in docking. • Various CNN backbones are benchmarked on photorealistic ISS refuelling datasets. • ResNet-101 reports average 1.19% error in position, 0.29 ∘ in attitude per trajectory. • Prototypes of the developed mechanism are validated in lab with 7-DOF manipulator. [ABSTRACT FROM AUTHOR]

Recently, computer vision and artificial intelligence are being used as enabling technologies for plant phenotyping studies, since they allow the analysis of large amounts of data gathered by the sensors. Plant phenotyping studies can be devoted to the evaluation of complex plant traits either on the aerial part of the plant as well as on the underground part, to extract meaningful information about the growth, development, tolerance, or resistance of the plant itself. All plant traits should be evaluated automatically and quantitatively measured in a non-destructive way. This paper describes a novel approach for identifying plant roots from images of the root system architecture using a convolutional neural network (CNN) that operates on small image patches calculating the probability that the center point of the patch is a root pixel. The underlying idea is that the CNN model should embed as much information as possible about the variability of the patches that can show chaotic and heterogeneous backgrounds. Results on a real dataset demonstrate the feasibility of the proposed approach, as it overcomes the current state of the art. • Root systems must be monitored to assess the growth and well-being of a plant. • State-of-the-art approaches mainly use classic ML or U-networks for segmentation. • CNNs can be used for monitoring considering patch-based information. • These models are simpler and faster, and provide better segmentation performance. [ABSTRACT FROM AUTHOR]

The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation. [Display omitted] [ABSTRACT FROM AUTHOR]

X-ray computed tomography (XCT) has been shown to be a reliable tool for quality inspection, material evaluation, and dimensional measurement tasks across diverse academic and industrial applications. In recent years, the integration of machine learning and deep learning techniques have ushered new advances in the industrial computed tomography domain spanning multiple facets, including image reconstruction, segmentation, and feature characterization. This review paper comprehensively surveys the current state-of-the-art machine learning and deep learning applications throughout the entire XCT workflow. Additionally, we explore relevant developments in the medical imaging domain, evaluating their implications for industrial computed tomography. In conclusion, we identify potential future research, drawing insights from existing research gaps in the domain and recent advancements in artificial intelligence. Notably, we underscore the importance of uncertainty quantification and model explainability for further acceptance of artificial intelligence techniques in the domain. [ABSTRACT FROM AUTHOR]

• AI improves infectious waste management. • Ensemble model exceeds 97.81% accuracy. • Reinforcement learning optimizes classification. • Geometric augmentation boosts model robustness. • Study addresses urgent urban waste challenges. This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices. [ABSTRACT FROM AUTHOR]