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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]

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]

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]

• A framework combining machine learning models and SHAP to interpret how peer review improves the research impact. • The importance of key linguistic, sentiment, and peer review features from peer review reports in determining the scientific significance of papers. • Valuable insights for authors to improve the quality of work and increase academic influence by paying closer attention to peer review characteristics. • Textual features of peer review reports play an important role in predicting post-publication scientific impact. Peer review is crucial in improving the quality and reliability of scientific research. However, the mechanisms through which peer review practices ensure papers become top-cited papers (TCPs) after publication are not well understood. In this study, by collecting a data set containing 13, 066 papers published between 2016 and 2020 from Nature communications with open peer review reports, we aim to examine how textual features embedded within the peer review reports of papers that reflect the reviewers' emotions may predict the papers to be TCPs. We compiled a list of 15 textual features and classified them into three categories: peer review features, linguistic features, and sentiment features. We then chose the XGBoost machine learning model with the best performance in predicting TCPs, and utilized the explainable artificial intelligence techniques SHAP to interpret the role of feature importance on the prediction results. The distribution of feature importance ranking results demonstrates that sentiment features play a crucial role in determining papers' potential to be highly cited. This conclusion still holds, even when the ranking of the feature importance changes in the subgroup analysis of dividing the samples into four disciplines (biological sciences, health sciences, physical sciences, and earth and environmental sciences), as well as two groups based on whether reviewers' identities were revealed. This research emphasizes the textual features retrieved from peer review reports that play role in improving manuscript quality can predict the post-publication research impact. [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]

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]

Based on the rapid development of intelligent technologies in recent years, the digital transformation of the whole industry and society has become increasingly important. Among them, digital twins and artificial intelligence have great potentials in improving industry processes and further enhancing productivity. This paper proposes an Intelligent Digital Twin System (IDTS) based on artificial intelligence and digital twins for the paper industry. The system includes the prediction models for the stirring speed of the dump chest, the water consumption of the deflaker, the supply air pressure of the dryer, and the exhaust air temperature of the dryer. The sensors, 5G network slices, and other equipment collect related data during the papermaking process for generating twin data, and we use the prediction models to analyze the data and monitor important indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) for the manufacturing processes, which are used to improve the energy utilization and production efficiency of the paper industry and thus facilitate cost saving. We apply this intelligent digital twin system and its associated prediction models to an actual paper manufacturing factory and show their effectiveness by improving the operational efficiency and saving labor and maintenance costs. [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]

This article introduces a nonparametric methodology combining the strengths of binary regression and latent variable formulations, while overcoming their disadvantages. The mathematical results are implemented through a novel Bayesian Hierarchical estimation methodology called Latent Adaptive Hierarchical Expectation Maximization Like algorithm. Requiring minimal assumptions, it extends extant methodologies, and in simulation studies gives better prediction and inference performances for asymmetric data generating processes. A new classification statistic, called Adjusted Receiver Operating Curve Statistic is also introduced. Utilizing it we demonstrate better overall model fit, inference and prediction performance of the proposed methodology over widely used existing methods in the sciences. In addition, the methodology can be used to perform model diagnostics for any model specification. This is a highly useful result, and it extends existing work for categorical model diagnostics broadly across the sciences. Furthermore, the mathematical results also highlight important new findings regarding the interplay of statistical significance and scientific significance. Finally, the methodology is applied to identifying highly-cited papers in the social sciences in a joint estimation framework. The results indicate that the methodology outperforms widely used existing artificial intelligence and machine learning models with very few Monte Carlo iterations. In Scientometric application, it finds Journal Impact Factor to be more important than Keyword Popularity parameters for explaining citation outcomes in select social science fields. It further finds that the percentage change in Published Popularity may also help to explain citation outcomes in the field. The findings appear to be new to the Scientometric field. • Introduces new statistical method for regressions requiring few assumptions. • Outperforms existing artificial intelligence/machine learning algorithms. • Introduces ARS: A general statistic for predication/comparison of models. • Applies methodology to identify highly-cited papers in the social sciences. • JIF more important than keywords for getting cited in some social science fields. [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]

Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance. [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]

[Display omitted] • Deep learning enabled predictive nucleic acid amplification tests analysis. • On-site fluorescence signal processing powered by cloud computing. • Accurate prediction can be obtained using the early 22.5% data. • Approach can be universally extended to other areas of biomedical research. Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research. [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]

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in academic publishing. However, criticisms have long been leveled at this mechanism, mostly because of its poor efficiency and low reproducibility. Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this paper, we propose the concept and pipeline of automated scholarly paper review (ASPR) and review the relevant literature and technologies of achieving a full-scale computerized review process. On the basis of the review and discussion, we conclude that there is already corresponding research and preliminary implementation at each stage of ASPR. We further look into the challenges in ASPR with the existing technologies. The major difficulties lie in inadequate data, imperfect document parsing and representation, defective human–computer interaction, and flawed deep logical reasoning. Moreover, we point out the future directions and discuss the possible moral and ethical issues of ASPR. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans. • The concept and pipeline of automated scholarly paper review (ASPR) are proposed. • ASPR-related literature and technologies are reviewed and summarized. • Current challenges and future directions of ASPR are highlighted. • Morality and ethics of ASPR are provided. [ABSTRACT FROM AUTHOR]

The number of citations is often used to estimate the impact of a study. Previous studies have investigated what factors of publications affect citations and how they affect citations. However, the findings of the studies were unable to reach a consensus because of the limited sample size, domain, and measurement. This study reviewed previous studies that addressed factors influencing citations and then identified 14 measurable factors. Approximately 33 million publications from the Scopus database were used to train and validate a CatBoost model. A SHAP framework was used to interpret the trained model by focusing on how salient factors affect the number of citations. The results showed that the year is a significant factor affecting the citation but not the priority factor. A publication source was presented as the most important factor contributing to the citation. Several implications and strategic approaches to maximizing the impact of a study were discussed. • This study examines 14 factors that can influence the citation of papers. • CatBoost model and SCOPUS dataset are used to examine the influences. • SHAP interprets the model and suggests how the factors contribute to the citation. • The results show that selecting the right journal/conference is the most important. [ABSTRACT FROM AUTHOR]

Infectious diseases are a global concern for public health resulting in high rates of infection with subsequent health and socio-economic impacts through resulting morbidity and mortality. The emergence of such diseases has motivated researchers to develop cost-effective, rapid and sensitive analytical methods and devices to better understand the transmission routes of infections within populations. To this end, rapid and low-cost diagnosis and testing devices for infectious diseases are attracting increasing amounts of attention, e.g., through using paper-based analytical devices (PADs). In this paper, the recent development of PADs is critically reviewed both for the diagnosis of inviduals and population health, by using devices for testing wastewater. Finally, the review also focuses on PADs for the analysis of bacteria and viruses in wastewater, together with a discussion on thee future development of PADs for rapid diagnosis and wastewater surveillance. • The major infectious diseases and main diagnosis technology were summarized. • The current paper-based devices for infectious disease diagnosis and wastewater surveillance were reviewed. • The integrated paper-based devices for point-of-care diagnosis were discussed. • Artificial intelligence, big data analytics, and IoT technologies were discussed for the integration with paper sensors. [ABSTRACT FROM AUTHOR]

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, –C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory. [Display omitted] • Five Rh antigens typing PADs combined with deep learning algorithm for an assisted human-decision. • Rh antigens typing PADs were tested with real blood samples and compared with the standard tube method. • Dataset of photographs following Rh PADs testing were trained and evaluated by the in-house deep learning platform CiRA CORE. • Misinterpretation caused by weak or ambiguous reactions was solved by the CiRA CORE platform. [ABSTRACT FROM AUTHOR]

Over the past decades, companies have continually sought out approaches that help them reduce the innovation cycle time due to its importance in their success. Among these approaches are TRIZ-based systematic inventive design processes, such as Inventive Design Methodology (IDM). Nevertheless, the application of these methods in the initial analysis, which requires an exhaustive gathering of information at the start of the innovation project without considering its impact on the final solution, decreases the agility of these types of processes. Consequently, a lean-based method called Inverse Problem Graph (IPG) has been proposed to formulate problems in the initial analysis phase of the inventive design process. However, the manual gathering of essential information to create a network of problems in the IPG method requires time and effort, which impresses the method's capability. This paper integrates an automatic information retrieval approach, using Doc2vec and Cosine Similarity as Artificial Intelligence methods and Scientific Papers, into the IPG process. The integration helps to introduce a new method for the initial analysis phase of inventive design, helping to resolve part of its drawback in collecting essential knowledge from Scientific Data. The capability of the proposal is then tested through an application using a Lattice Structure case study. • This study introduces an automatic information retrieval-based method to formulate a problem situation. • It integrates the doc2vec model and the cosine similarity method into the inventive design process. • The proposal facilitates and accelerates problem formulation in the inventive design. • The proposed method increases the accuracy of problem formulation in the inventive design. [ABSTRACT FROM AUTHOR]

The emerging concept of digital twins outlines the pathway towards intelligent buildings. Although abundant building data carries an overwhelming amount of information, if not well exploited, the redundant and irrelevant data dimensions result in the overfitting problem and heavy computational load. Taking the fault detection and diagnosis process for building HVAC systems as the case, this paper adopts a symbolic artificial intelligence technique to identify informative sensory dimensions for building-specific faults by exploring the symbolic representation of labelled time-series. To preserve this ad-hoc temporal knowledge in the digital twin ecosystem, machine-readable fault tags are defined to label corresponding sensor entities. A digital twin data platform is developed to annotate the real-time data with fault tags and produce filtered low-latency data streams associated with a specified tag to automate this process. This paper describes a digital twin-based approach to automatically identify and pick up informative data to support dynamic asset management. • Adopt tagging to represent and preserve knowledge learned through AI techniques. • Use Bag-of-Words to identify informative sensory dimensions for case-specific faults. • Define machine-readable fault tags to label the sensors and annotate real-time data. • Produce low-latency data streams appended with the specified tags to feed the FDD. • Informed a way to enable asset management functionalities through digital twin. [ABSTRACT FROM AUTHOR]

3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation , network structure design , and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations. [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]

Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of artificial intelligence (AI) tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing deep learning (DL) methods designed for the automatic screening of diabetic retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset consists of color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of DR and diabetic macular edema. Each of the chosen papers concentrates on various DR signs. Nevertheless, a significant portion primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify DR signs. Among the chosen papers, 4.7 % utilized detection methods, 46.5 % employed classification techniques, 41.9 % relied on segmentation, and 7 % opted for a combination of classification and segmentation. Metrics calculated from 80 % of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple DL techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification, and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes; however, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR, but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining the high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to develop new approaches and overcome existing limitations. By doing so, a full potential of AI may transform DR screening and improve patient outcomes. [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]

[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]

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]

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]

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]

The use of digital technologies in managing bowel conditions has been a topic of interest among healthcare practitioners. The objectives of this paper were to provide information about the types of digital technologies that have been used for bowel management and the context of the studies; identify the gaps and challenges in digital technologies for bowel management and propose new methods and techniques for the application of digital technologies in bowel management. A scoping review was conducted following the principles of Preferred Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A search was conducted on six academic databases. 1891 papers were retrieved from the initial search; however, 6 papers were included based on the inclusion and exclusion criteria. The findings suggest that published work focused mainly on a research context and with a narrow focus targeting sub-categories of bowel conditions and not implemented in the context of everyday use. The findings also illustrate the variety of early-stage developments focused on increasing support for severe bowel dysfunction, for example, through biofeedback to aid muscle control training, or the placement of artificial anal sphincters to increase rectal perception. However, technology to support bowel management for broader populations with less severe or variable symptoms appears limited. Future work would be to conduct empirical research in the application of advanced technologies such as on-organ sensors in managing bowel conditions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a better understanding about the benefits that can be realized after implementing voice-based chatbot technologies in banks. Furthermore, this paper aims to investigate the challenges faced by organizations when implementing such technologies. This study followed a qualitative approach where semi-structured interviews were conducted with experts from mid to senior managerial levels working in a leading bank operating in Jordan and had implemented the voice-based chatbot technology. Implementing voice-based chatbot technology for the first time in a Jordanian bank has positively impacted the growth of the bank, increased customers satisfaction, and added a competitive advantage value to the bank. On the other side, different challenges faced the bank when it implemented this technology like issues related to security, compliance, integration, and infrastructure limitations. This paper can be considered in the foremost research conducted to investigate the implementation of AI-based chatbot in Jordanian banks. It demonstrated the benefits and challenges resulting from performing digital innovations introduced as innovative services in the banking sector that can inform the experience to other banks interested to implement such technologies. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology with Big data, Artificial Intelligence, and Machine Learning. It disrupted industries such as health, education, manufacturing, and banking. However, the increasing popularity of Blockchain ex- poses the scalability issues of major public blockchain platforms (e.g., Bitcoin and Ethereum) and dramatically affects its development. The scalability problem manifests in terms of Low throughput, high transaction latency, and massive energy consumption. Several reviews and studies cover these factors and their potential solutions, yet these studies need to highlight more information through actual application to natural systems or projects. This study investigates all relevant papers on current research solutions for public blockchain scalability issues. The scope of this paper is to explore the implementation of different state-of-the-art scalability solutions to natural systems and projects while simultaneously highlighting the results. This study discusses the methods and techniques used and the challenges encountered that have yet to future researchers must explore. [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]

Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing. [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]

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]

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]