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Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

In augmented reality (AR) headset devices, virtual image visibility and power consumption are major challenges. In this paper, we presented smart pixelated dimmer that locally enhance the ambient contrast ratio. The dimmer is integrated with image sensor and low‐power edge AI processor to control the dimming automatically. Adopting smart pixelated dimmer provides AR devices the possibility to operate in high brightness environment. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self‐contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low‐power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub‐second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper‐based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time‐series forecasting tasks are conducted. The memory capacity of short‐term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability. [ABSTRACT FROM AUTHOR]

The article discusses the need for improvement in the peer review system for evaluating submitted manuscripts. The authors propose a new system to track the outcomes of rejected manuscripts, allowing for a better understanding of their "fates." They suggest that authors should report to the journal that finally accepted their manuscript where it was previously rejected. This system aims to increase transparency in the academic publication world and improve the evaluation skills of reviewers, editors, and journals. [Extracted from the article]

This article discusses OpenAI's ChatGPT, a generative pre‐trained transformer, which uses natural language processing to fulfill text‐based user requests (i.e., a "chatbot"). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT‐3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer‐reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.Practitioner notesWhat is already known about this topicEmotions play a pivotal role in shaping the interaction process, making it essential to consider them when designing methodological strategies or learning tools.Empathic Pedagogical Conversational Agents (PCAs) have emerged as a crucial approach for enhancing and personalizing the learning experience (24/7) for pupils and supporting human teachers in their teaching process.Despite the creation of numerous empathic PCAs, there is a scarcity of Systematic Literature Reviews (SLRs) on their application in the educational field, particularly concerning the integration of emotional abilities in combination with the competencies of each subject.What this paper addsIt offers new insights into the design principles underlying the integration of the empathic field.It reviews different approaches for incorporating students' prior knowledge in real time.It provides a comprehensive and up‐to‐date overview of the research designs used for implementation, including quantitative, qualitative and mixed methods.It examines the factors that influence the effectiveness of empathic PCA in teaching and learning.It evaluates the types of feedback that enhance the impact of the empathic field on learning outcomes.Implications for practice and/or policyIt is crucial to grasp the topics that this paper introduces in order to effectively integrate new learning tools into any context.Techno‐pedagogical designers seeking to gain insights into empathic PCAs will find immense value in this SLR, as it comprehensively covers each stage of the process.For future research endeavours, this study offers a wealth of ideas to draw upon, enabling researchers to address the challenges outlined and explore new avenues of investigation. [ABSTRACT FROM AUTHOR]

Drug information specialists (DIS) bring unique, specialized expertise and provide services in diverse settings including health systems, academia, pharmaceutical industry, compendia, medical writing, and other areas. With widespread access to drug information (DI) resources through user‐friendly, online platforms, the role of DIS has shifted. Core skill sets once confined to DIS are now distributed across various non‐DIS clinical specialties. DIS have transformed the application of their specialized skill set and adapted it to a variety of traditional and nontraditional areas, providing and applying advanced expertise to solve a variety of contemporary challenges. The training of students and residents has evolved to include evidence‐based practical contemporary methods that promote critical thinking and reasoning. Effective DI evaluation and communication necessitates customizing content for stakeholders to ensure understanding and contribute to optimal patient care, all while addressing misinformation and disinformation. The future of DI as a specialty is bright, with ever‐increasing recognition of the importance of DI skills in non‐DIS practitioners. DIS will likely continue to guide best practices in the education/training of new practitioners and continue to provide advanced services and formulary analytics. This update to our 2009 DI PRN Opinion paper will focus on: (1) DI education and training needed for all students, residents, and pharmacists; (2) career opportunities, roles, and responsibilities specifically tailored for DIS in health systems, managed care organizations, academia, pharmaceutical/biotechnology industry, and medical writing services; and (3) the future direction of DI, including the potential impact of artificial intelligence. [ABSTRACT FROM AUTHOR]

This Editorial introduces a Special Issue of papers from the SEE International Conference RADAR 2019, which took place in Toulon, France, 23–27 September 2019. The papers are expanded versions of ones that were judged in the reviewing process to be of the highest quality and relevance. Benefitting from the unique location of the conference centre on the French Riviera, the conference was given the theme 'Sensing from sea to space', attracting nearly 400 attendees from 26 countries from all over the world (Figure 1). Engineers and researchers coming from private or public sector (universities and laboratories) shared their knowledge and experience on radar topics of common interests from physics to signal processing, from sensor validation to system design, and also on emerging technologies based on artificial intelligence; all topics being supported by 200 papers presented either in oral (150 papers in 36 sessions), or poster sessions (50 papers). Figure 2 shows the range of topics of the papers. 1 FIGURE: Conference attendees by country 2 FIGURE: Conference paper topics [ABSTRACT FROM AUTHOR]

This paper presented intelligent display technologies integrated in the 55‐inch 4K LCD cell based on a‐Si process. Adaptive adjustment of backlight brightness and optimization of LC response time were achieved utilizing the ALS and TS integration. By using the novel GOA architecture and TCON IC, dynamic local refresh at any position and with different refresh rate at different area on panel were achieved. The power consumption of GOA and data drivers can be reduced by up to 50% respectively. This technology provides hardware foundation for the realization of AI intelligent display. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

The letter to the editor discusses the use of ChatGPT, an AI language model, in medical writing. The author argues that while ChatGPT can be helpful for non-native English speakers, it is important to be cautious of relying too heavily on it. Doctors who heavily rely on ChatGPT may struggle to accurately convey their intended nuances and may not fully understand the details of referenced articles. The author suggests that doctors should write a rough draft themselves and seek linguistic help from experienced mentors or commercial services. The author expresses concern that the emergence of ChatGPT may undermine the opportunity for younger doctors to develop their writing skills. [Extracted from the article]

Holographic near‐eye displays promise unprecedented capabilities for virtual and augmented reality (VR/AR) systems; they can accurately reproduce focus cues, improve power efficiency, and correct for optical aberrations. However, the image quality achieved by traditional holographic displays is limited, and algorithms for computer‐generated holography (CGH) are slow. In this paper, we review emerging artificial intelligence‐enabled holographic near‐eye displays that promise to solve these long‐standing challenges. [ABSTRACT FROM AUTHOR]

3D display technology can bring users 3D stereoscopic and realistic visual experience. At present, many types of 3D display technologies have been developed in this field. Among them, naked eye 3D is a more advanced 3D display technology. Compared with traditional polarized 3D display technology, it allows people to directly observe the stereo vision effect without polarized glasses. The naked-eye 3D technology discussed in this paper is based on micro-lens array, which is an important branch in the field of naked-eye 3D display. In this paper, the real-time human eye tracking technology is used to capture the user's human eye coordinates, and the pixel layout on the display module is adjusted in real time according to the human eye coordinate position, so as to ensure that the images taken into the human eye within any visual angle range have a three- dimensional effect. BOE has accumulated many advanced technologies in the field of display and artificial intelligence, especially in the fields of high-resolution display, new display and human eye tracking. This paper focuses on the naked eye 3D display system independently developed by BOE. [ABSTRACT FROM AUTHOR]

The article provides a preview of forthcoming papers accepted for publication in the Health Information and Libraries Journal. Topics include the effectiveness of citation searching in urgent and emergency care configuration studies, an alternative screening approach for Google Search in systematic reviews, and the analysis of health information seeking behavior among adolescents.

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

The article discusses the use of chatbot technology in the field of microscopy. The authors, Filip Braet and David Poger, highlight the potential benefits of chatbots in providing access to academic information and datasets. However, they also acknowledge the challenges and ethical considerations associated with using this technology in research. The authors share their experiences working with chatbots and express curiosity about the future role of AI in microscopy. They emphasize the importance of responsible and ethical use of AI in research and education. [Extracted from the article]

We report a novel OLED material discovery process and several applications based on AI technology. This process in which six AI modules that generate molecular structures with active learning algorithm, predict multiple properties, analyze novelty, predict synthetic scheme, predict relative synthesizability and predict device characteristics are linked one after another. Also, we introduce some cases in which materials designed by this process were actually synthesized and applied to devices for evaluation to confirm the improvement of characteristics. [ABSTRACT FROM AUTHOR]

As a replacement for the lecture-driven teaching of the past, we are aiming to establish an individually initiated, efficient independent study environment using a computer network. This paper focuses in particular on the collection of Web information needed by researchers, based on efficient paper retrieval and collection Web sites using XML. XML can handle data on the Web more flexibly than the present HTML, and research into its practical applications is progressing rapidly. In the artificial intelligence field, ontology engineering for authoring tools to be used in creating teaching materials is under way. In this context, because XML has been defined by the W3C, it is an accessible standard, and application examples have appeared. However, few of them focus on retrieval. We describe a “paper retrieval system” for efficient retrieval, constructed only in HTML on the basis of XML, RDF, and XHTML. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 34(1): 21–28, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10014 [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Holography promises unprecedented capabilities for augmented reality (AR) display systems, but the current achievable image quality is quite limited. We leverage Neural Holography, a family of artificial intelligence‐driven computer‐generated holography (CGH) techniques, to enable two holographic see‐through AR displays: a lightguide coupled near‐eye display (NED) and a partial reflective head‐up display (HUD). Experimental results demonstrate the promise of enabling next‐generation AR 3D holographic displays. [ABSTRACT FROM AUTHOR]

Both Model‐Based Systems Engineering (MBSE) and Artificial Intelligence (AI) have been challenged for their deployment in real‐world applications. Although MBSE remains the focal point of any systems engineering activities, its adoption still faces significant hurdles to demonstrate its return on investment. Recently, AI has received intensive attention, and its applications made their way into our daily life products. From an industrial perspective, within the context of the design and development of mechatronic systems, there is a lack of coherent foundation to enable the application of AI in MBSE. This vision paper discusses the role of AI in solving a set of MBSE challenges. As a result, we contribute by describing the actual MBSE adoption challenges and follow up with the characterization of the capabilities of AI in solving these challenges. With this initial work, we aim to trigger both AI and MBSE communities for further research discussions and industrial applications to help in achieving an intelligent design and development environment. [ABSTRACT FROM AUTHOR]

With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

The papers address the following key areas: ' B I Applications of IoT and digital twin in electrical power systems: A comprehensive survey i b ': This research paper reviews the applications of the Internet of Things (IoT) and digital twin technology in electrical power systems. With the growth of Internet of Things (IoT) techniques, applications have become smarter, and linked gadgets allow them to be used in many parts of power systems. ' B I A new low-cost and low-power industrial Internet of Things infrastructure for effective integration of distributed and isolated systems with smart grids i b ': This research paper provides an Internet of Things (IoT) infrastructure solution based on a newly designed low-cost microcontroller-based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. [Extracted from the article]

The accuracy of short‐term photovoltaic (PV) power prediction is crucial for maintaining power system stability and grid scheduling. Here, a short‐term PV power prediction framework is proposed considering combined weather similarity day screening, signal decomposition noise reduction and hybrid deep learning to realize PV power prediction. First, a combined meteorological similar day screening model is constructed to screen out historical days similar to the day, which reduces the number of training sets; Second, Synchrosqueezing Wavelet Transform is utilized to eliminate data noise. Third, a Convolution Neural Network‐Gate Recurrent Unit (CNN‐GRU) network is constructed to extract periodic and nonlinear features in the PV power generation data series and to capture the relationship features between PV power generation and meteorological factors to improve the prediction accuracy. Fourth, the Sparrow Search Algorithm is introduced to perform hyper‐parameter optimization of the CNN‐GRU network to accelerate the model convergence and improve the model training efficiency. Finally, this paper conducts simulation experiments and the experimental results show that the prediction method proposed in this paper can effectively improve the prediction accuracy of short‐term PV power compared to the baseline model, and the method proposed in this paper is superior to other conventional methods. [ABSTRACT FROM AUTHOR]

The article discusses recent advancements in artificial intelligence (AI) and their impact on speech and language comprehension. It highlights the emergence of speech and language technologies as major topics in AI research, enabling computers to interpret human language in both written and spoken forms. The article also provides summaries of 12 papers included in a special issue on speech and language technologies, covering topics such as question answering, coreference resolution, essay grading, named entity recognition, morphological analysis, code generation, Alzheimer's disease recognition, backchannel prediction, spoken-to-written text conversion, multimodal speech recognition, and language tutoring systems. The authors express their gratitude to the contributors and emphasize the importance of these studies for the future design and implementation of AI systems. [Extracted from the article]

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

In this paper, simulation‐based artificial intelligence is applied to discover new materials, and to aid in backplane and module design in order to realize high performance organic light emitting diode displays. Additionally, image quality restoration based on deep learning is introduced for a new display form factor in which the camera is positioned beneath the display panel. [ABSTRACT FROM AUTHOR]

In this paper, we propose a binary feedback scheme for congestion control in computer networks using a gradient-based mechanism which employs neural network modelling of the system dynamics. The optimal direction for rate adjustment at the source is based on a single bit feedback signal which depends upon the sign of the sensitivity of the system performance index with respect to queue input rate. The paper presents the scheme and the analysis that went into the choice of the various decision mechanisms. Simulation results are presented to show the performance of the gradient based technique as compared to the conventional queue-based approach. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Bibliometric analysis was performed to study the development of publications related to Industry 4.0 and its key technologies in Vietnam. Comparisons with data from other ASEAN countries, and with global data have been done to identify distinctive characteristics of Industry 4.0 literature from Vietnam. The collection of 1,470 retrieved papers was analysed to answer seven research questions. Our results highlighted some valuable insights of Industry 4.0 literature in Vietnam. The number of papers in Industry 4.0 in Vietnam increased rapidly in recent years, mostly focused on Computer Science, Engineering, and Mathematics. Iran, China, and South Korea were the most productive partner countries with Vietnam in Industry 4.0. Machine learning, artificial intelligence, big data, deep learning, Internet of things, neural networks, and data mining were among the most popular research themes in Industry 4.0 in Vietnam. Vietnam ranked third among 10 Southeast Asian countries, based on the number of published papers in Industry 4.0, but the gap with the two top countries was large. Compared to the global data, the annual growth rate of Industry 4.0 papers in Vietnam, and other Southeast Asian countries was lower. Findings from this work can be helpful for other scholars in establishing potential future research lines related to Industry 4.0 in Vietnam. [ABSTRACT FROM AUTHOR]

Large format IoT displays support a range of exciting visual experiences which are encountered in our day‐to‐day lives. Four processor roles, media player, display controller, touch controller and CV/AI module, leverage four processor types, scalar, vector, spatial and matrix, to power these experiences. Algorithm characteristics such as arithmetic, memory access and data‐dependence complexity influence the suitability of an architecture to a workload. Including, GPUs to rendering, CPUs to interactive application, and FPGAs to custom video pipelines. Programmatic advertising, touch‐less kiosks and CODEC enhancements to minimize bitrate of 8K video streams can require inference (AI) workloads. Matrix (I PU) type processors are designed to address this workload type. The narrow pitch LED wall display market is growing at 10x the overall video wall display market rate and leverages spatial FPGA processing in LED controllers, also requiring 8K resolution at smaller surface area sizes as pixel pitch reduces. Requirements on each heterogenous processor type is concluded to be growing with the combination of market trends. [ABSTRACT FROM AUTHOR]

Large format IoT displays support a range of exciting visual experiences which are encountered in our day‐to‐day lives. Four processor roles, media player, display controller, touch controller and CV/AI module, leverage four processor types, scalar, vector, spatial and matrix, to power these experiences. Algorithm characteristics such as arithmetic, memory access and data‐dependence complexity influence the suitability of an architecture to a workload. Including, GPUs to rendering, CPUs to interactive application, and FPGAs to custom video pipelines. Programmatic advertising, touch‐less kiosks and CODEC enhancements to minimize bitrate of 8K video streams can require inference (AI) workloads. Matrix (VPU) type processors are designed to address this workload type. The narrow pitch LED wall display market is growing at 10x the overall video wall display market rate and leverages spatial FPGA processing in LED controllers, also requiring 8K resolution at smaller surface area sizes as pixel pitch reduces. Requirements on each heterogenous processor type is concluded to be growing with the combination of market trends. [ABSTRACT FROM AUTHOR]

With the development of 5G technology, artificial intelligence and autonomous driving technology, cars are gradually transforming to intelligent and digital. Once driverless technology is implemented, the cars are likely to become a third space. In this third space, whether it is a passenger or a driver, the demand for office and entertainment will continue to grow. So having a high definition screen and a good human‐computer interaction system are essential for this cars. As cars move towards digitalization, there are more and more screens in the cars, and more and more dual screens or multiple screens are used in the cluster and center control area. But there are different levels of chromatic aberration between different screens and the interaction between different screens is more complicated. A larger screen can achieve better optical taste and make the interaction between different function areas easier, it is becoming a trend to replace the dual or multiple screens. LTPS(Low Temperature Poly Silicon) has the higher electronic mobility, it is easier to achieve high resolution, high frequency, in‐cell touch integration and large screen. The displays based on LTPS technology can meet the development demands of vehicle displays, will be more and more widely used in the automotive display field. This paper will introduce the application of LTPS technology in automotive medium and large size display. [ABSTRACT FROM AUTHOR]

For example, a manuscript that examines the application of machine learning methods in reviewing irregularity reports from test administration is in scope. Machine learning, or artificial intelligence as a broader term, has been very popular in recent years. [Extracted from the article]

Learning-driven ubiquitous mobile edge computing: Network management challenges for future generation Internet of Things Keywords: artificial intelligence; Internet of Things; machine learning; mobile edge computing; network Management EN artificial intelligence Internet of Things machine learning mobile edge computing network Management 1 4 4 09/13/23 20230901 NES 230901 Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. Fifth, I Jafar et al. i proposes "A blockchain-enabled security management framework for mobile edge computing", which guarantees secure data storage and includes blockchain features like immutability and traceability. [Extracted from the article]

Deep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross‐modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two‐branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency. [ABSTRACT FROM AUTHOR]

Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process‐based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system. Plain Language Summary: Artificial Intelligence is a rapidly advancing field, in which Interpretable Machine Learning (IML) is seen as having the potential to significantly improve our understanding of Earth's complex environmental systems. IML goes beyond the predictive power of machine learning models, focusing instead on uncovering the relationships within the data that are revealed by the model's learning process. However, there is still a lack of straightforward, practical domain‐specific guidelines for geoscientists that facilitate both broader and more careful application in the field. In this paper, we aim to demonstrate the real‐world benefits of IML in typical geoscientific analysis. We provide a clear, step‐by‐step workflow that shows how IML can be used to address specific questions. We also point out some common pitfalls in using IML and offer solutions to avoid them. Our goal is to make IML more accessible and useful to a wider range of geoscientists, and we believe that IML, if used properly and thoughtfully, can become an essential and valuable tool to advance our understanding of complex Earth systems. Key Points: We demonstrate the broader relevance of Interpretable Machine Learning (IML) to most geoscientists and underexplored opportunities for its useWe describe a workflow for the effective use of IML while cautioning against potential and common pitfallsWe suggest good practices for its adoption and advocate for more careful application to ensure reliable and robust insights for the field [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy. [ABSTRACT FROM AUTHOR]

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided. [ABSTRACT FROM AUTHOR]

As one of the major causes of antimicrobial resistance, β‐lactamase develops rapidly among bacteria. Detection of β‐lactamase in an efficient and low‐cost point‐of‐care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)‐assisted mobile health system that consists of a paper‐based β‐lactamase fluorogenic probe analytical device and a smartphone‐based AI cloud. An ultrafast broad‐spectrum fluorogenic probe (B1) that could respond to β‐lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three‐dimensional microfluidic paper‐based analytical device was fabricated for integration of B1. Also, a smartphone‐based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the β‐lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem‐solving ability in interdisciplinary research, and demonstrates potential clinical benefits. [ABSTRACT FROM AUTHOR]

Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods. [ABSTRACT FROM AUTHOR]

However, as host-switch graph cannot represent such systems, the authors investigate interconnection networks with multi-port hosts by introducing a multi-ported host-switch graph. The past years have seen a flurry of activity in the area of distributed computing and its related fields. The accepted papers for this special issue address the following aspects in computing and networking: Parallel and distributed computing Security and privacy Interconnection networks Artificial intelligence Self-stabilizing algorithms Parallel and distributed computing The first contribution, entitled "Efficient Parallel Implementations to Compute the Diameter of a Graph" by Takafuji et al., presents an efficient implementations of the Blocked Floyd-Warshall algorithm, which uses no barrier synchronization and invokes only one kernel call by using Single Kernel Soft Synchronization (SKSS) techniques. [Extracted from the article]

We developed a neural network‐based method for evaluation of display luminance and color non‐uniformity (which we call Mura). We studied a correlation between our developed method and human visual evaluation because visual evaluation is the gold standard for Mura evaluation. We achieved Pearson correlation coefficient of 0.82. [ABSTRACT FROM AUTHOR]

Provides information on the state of intelligent systems research in Ukraine. Research groups listed by the National Academy of Sciences information center; Centers affiliated to the V.M. Glushkov Institute of Cybernetics; List of publications that universities and scientific institutes have throughout the country.

Recent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain’s visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we also hope to provide inspiration for future advances in automated visual processing. [ABSTRACT FROM AUTHOR]

The analysis of magnetic near‐field radiation emissions (MNRE) has recently raised more attention in device‐level electromagnetic compatibility testing. Pattern clustering of MNRE for integrated circuits manually is very time‐consuming because of the multi‐dimensional characteristics of MNRE, such as frequency, spatial position, emission intensity, etc. This paper proposes a novel pattern clustering method of MNRE, including strong emission frequency extraction, feature extraction, and density‐based clustering. Ring oscillator and five working states are designed on a Field Programmable Gate Array with 256 Ball Grid Array package, which are used to create a complex multi‐source emission case for verifying the effectiveness of the clustering method. The verification results show that the proposed method can correctly cluster the multi‐source emission patterns. Further, the method is also applied to a Microcontroller Unit with unknown operating states; the results show that the proposed method also can effectively distinguish the unknown emission patterns and locate the unknown interference source. The accuracy of the interference source location is proven by 3D X‐ray microscope inspection. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence in smart grid operations, particularly for defect detection, has seen remarkable progress. However, effectively leveraging multi‐modal data sources to enhance defect detection performance remains challenging. This paper presents a novel Knowledge Graph‐based Multimodal Neural Network (KGMNN) approach for robust and efficient defect detection in smart grids. Our proposed method first leverages multiple heterogeneous data sources, such as structural data, temporal sensor readings, and maintenance records, creating a comprehensive representation of the grid system. The system is then modeled as a knowledge graph, with nodes representing various entities and edges signifying their interconnections and interactions. This paper introduces a novel neural network architecture that effectively leverages the knowledge graph structure. This architecture incorporates multi‐modal fusion techniques, enabling the model to process and integrate diverse data sources. It can recognize intricate patterns indicative of potential defects that are often undetectable in individual data modes. Experimental results on several real‐world smart grid datasets demonstrate that the KGMNN significantly outperforms state‐of‐the‐art techniques in terms of precision, recall, and computational efficiency. Furthermore, the KGMNN's ability to provide explainable predictions, aided by the inherent interpretability of the knowledge graph structure, is of significant practical value in the field. This research paves the way for a new generation of AI‐powered defect detection in smart grids, facilitating enhanced grid reliability and operational efficiency. [ABSTRACT FROM AUTHOR]