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Objectives Methods Results Conclusion To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs.Qualitative semi‐structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022.We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision‐making process. There is considerably more approval of AI tools that support clinical decision‐making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision‐making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI.Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited. [ABSTRACT FROM AUTHOR]

Summary The increasing environmental issues such as climate change and air pollution require energy saving and emission reduction in various fields, such as manufacturing, building, and transportation. To address the above problem, proton‐exchange membrane fuel cells (PEMFC) gradually become promising green energy conversion device due to the advantages of zero pollution, high efficiency, and low operating noise. However, the durability problem has extremely limited the PEMFC large‐scale commercial application. To prolong the service life of PEMFC, performance degradation prediction is an effective method. This paper proposes a multi‐step performance degradation prediction method for proton‐exchange membrane fuel cells based on CatBoost feature selection, convolution computing, and interactive learning mechanism. CatBoost is used to evaluate the importance of the monitor parameters on performance degradation. The evaluation results and PEMFC degradation mechanism analyses are used to select the monitor parameters for construing the prediction model. Based on the 1D convolutional layer and the interactive learning mechanism, the prediction model is proposed to extract the deep features from the monitor data to predict the performance degradation of the fuel cell system. In particular, the multi‐step prediction is performed by the configurable sliding window. The effectiveness of the proposed method is verified on real experiment datasets, and the experiment results show that the proposed method is particularly effective for multi‐step degradation prediction and decreases the computation by feature selection and 1D convolution layer. [ABSTRACT FROM AUTHOR]

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction. [ABSTRACT FROM AUTHOR]

Background: Introducing new technologies in education sparks debates, disrupting traditional practices, and requiring teacher adaptation. ChatGPT is an example. Research explores its benefits and concerns in education, with recommendations for classroom use. Nevertheless, limited evidence supports ChatGPT as a tool for supporting English as a Foreign Language (EFL) students' research competency. Moreover, while the literature primarily focuses on the perspectives of scholars and teachers, it is essential to incorporate students' viewpoints in order to maximize the potential of this technology. Objectives: The present study investigated EFL undergraduates' perceptions and practices of ChatGPT as a tool for promoting their research competency. Additionally, the study examined the potential influence of metacognitive awareness (MA) levels in this context. Methods: The study utilized a mixed‐method research design, collecting quantitative and qualitative data from 27 EFL undergraduates (12 with low MA and 15 with high MA) over one semester. Data sources included a self‐report questionnaire, self‐study log, and semi‐structured interviews. Results and conclusions: Quantitative analysis showed significant differences between low and high MA groups in their perceptions of ChatGPT (U = 37.500, Z = 2.570). Regression analysis revealed a positive correlation between MA scores and students' perceptions and practices of ChatGPT. Qualitative analysis revealed diverse perceptions and practices of ChatGPT between the high MA and low MA groups, showcasing behaviours ranging from replication to using ChatGPT as a thought‐stimulating and supportive tool, accessing supplementary information, and seeking guidance. Key factors for effective ChatGPT use included prompt formulation, systematic thinking, self‐regulation strategies, and trust in AI. Lay Description: What is currently known about this topic: Research competency and metacognitive awareness are important for EFL undergraduates.Technology has been explored for promoting research competency.ChatGPT is used as a learning tool in EFL instruction contexts. What does this paper add: It provides a deeper understanding of the practical application of AI tools like ChatGPT for developing research competency among EFL students.It enhances understanding of metacognition's role in successful learning in digital environments by exploring MA's impact on using ChatGPT for research purposes. Implications for practice/policy: Provide explicit instructions and guidelines for EFL students to effectively use ChatGPT for research competency, emphasizing clear prompt design and fostering responsible use through critical thinking and awareness of biases and limitations.Enhance EFL students' metacognitive awareness through scaffolding, explicit instruction, and activities.Implement assessments and monitoring mechanisms to track metacognitive development and guide instructional interventions.Offer professional development opportunities for EFL teachers to enhance their understanding and skills in integrating AI tools into research instruction and other EFL aspects. [ABSTRACT FROM AUTHOR]

Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models. [ABSTRACT FROM AUTHOR]

Background: The use of artificial intelligence (AI) is increasing in many areas of healthcare, including mental healthcare. The automated nature of such technologies has the potential to be developed to work with large numbers of people. This paper examines the way that student therapists experience using an interactive text‐based machine client as a training tool. Methodology: Chatbot technology has been used to develop an emotionally responsive interactive client (ERIC). This introduces individuals to concepts of person‐centred therapy (empathy, congruence and unconditional positive regard) by using a series of pre‐programmed scenarios. Twenty‐eight student therapists evaluated ERIC's potential as a learning tool. Individuals were recruited from one university from a postgraduate and an undergraduate counselling programme. Findings: Feedback was generally positive, with all reporting that they enjoyed engaging with ERIC as a learning method. ERIC helped individuals consider their understanding of counselling skills in a non‐judgemental environment. Participants felt the scenarios were realistic and engaging, with many reporting that they felt they were engaging with a real client/person due to ERIC's ability to express emotions. Discussion: ERIC is at the proof‐of‐concept phase. From the feedback presented here, it is evident that it can be a useful learning tool. Further development of ERIC with feedback from a larger sample is, however, required. ERIC is currently a text‐based client, and further development would like to see the intervention be voice‐activated to enhance the experience. ERIC can be further enhanced and adapted to be a useful learning platform for student therapists, as well as for students in other (healthcare‐related) disciplines, whereby a client or patient is required. [ABSTRACT FROM AUTHOR]

Background and Objectives: Children undergoing airway management during general anesthesia may experience airway complications resulting in a rare but life‐threatening situation known as "Can't Intubate, Can't Oxygenate". This situation requires immediate recognition, advanced airway management, and ultimately emergency front‐of‐neck access. The absence of standardized procedures, lack of readily available equipment, inadequate knowledge, and training often lead to failed emergency front‐of‐neck access, resulting in catastrophic outcomes. In this narrative review, we examined the latest evidence on emergency front‐of‐neck access in children. Methods: A comprehensive literature was performed the use of emergency front‐of‐neck access (eFONA) in infants and children. Results: Eighty‐six papers were deemed relevant by abstract. Finally, eight studies regarding the eFONA technique and simulations in animal models were included. For all articles, their primary and secondary outcomes, their specific animal model, the experimental design, the target participants, and the equipment were reported. Conclusion: Based on the available evidence, we propose a general approach to the eFONA technique and a guide for implementing local protocols and training. Additionally, we introduce the application of innovative tools such as 3D models, ultrasound, and artificial intelligence, which can improve the precision, safety, and training of this rare but critical procedure. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence. [ABSTRACT FROM AUTHOR]

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the center stage of philosophical debates about cognition. This development is directly relevant to long‐standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.This article is categorized under: Philosophy > Artificial Intelligence Computer Science and Robotics > Machine Learning [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is influencing different aspects of human life. An AI‐powered technology, which has been recently released, is ChatGPT. It is a cutting‐edge technology that influences second/foreign language (L2) education. Although there is increasing research on the benefits and misfits of this chatbot in different disciplines, L2 education lacks a thorough investigation. To fill this lacuna, this phenomenographic study examined the perceptions of research‐active English as a Foreign Language (EFL) teachers regarding the potentials and pitfalls of ChatGPT for L2 learning, teaching, assessment, and research. To this end, a semistructured interview was held with 30 Iranian EFL teachers with varying educational backgrounds and AI integration experiences. The results of content and thematic analysis indicated that ChatGPT is a double‐edged sword that can both benefit and hurt these areas of L2 education. The most notable potentials were augmenting learner autonomy, providing personalized learning, reducing teachers’ teaching workload, designing assessment rubrics, and summarizing lengthy papers and theses to save L2 researchers’ time and energy. Concerning pitfalls, it was reported that ChatGPT might kill creativity and academic integrity, encourage cheating in online exams, spread fake and misinformation into the world of research, and cherish high‐tech plagiarism. Some practical suggestions are made to empower L2 educators and researchers to survive in the world of AI. [ABSTRACT FROM AUTHOR]

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. [ABSTRACT FROM AUTHOR]

Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID. [ABSTRACT FROM AUTHOR]

Most existing multi‐view stereo (MVS) methods fail to consider global context information in the stage of feature extraction and cost aggregation. As transformers have shown remarkable performance on various vision tasks due to their ability to perceive global contextual information, this paper proposes a transformer‐based feature enhancement network (TF‐MVSNet) to facilitate feature representation learning by combining local features (both 2D and 3D) with long‐range contextual information. To reduce memory consumption of feature matching, the cross‐attention mechanism is leveraged to efficiently construct 3D cost volumes under the epipolar constraint. Additionally, a colour‐guided network is designed to refine depth maps at a coarse stage, hence reducing incorrect depth predictions at a fine stage. Extensive experiments were performed on the DTU dataset and Tanks and Temples (T&T) benchmark and results are reported. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy.Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy.Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes.What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions.Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy.Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy.Practitioners are able to use the AILQ to assess students' AI literacy development. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notesWhat is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.Describes how we adapted six design methods to design AI features.Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.Our work also includes a reflection on our design process and implications for future design processes. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

Image registration is a fundamental task in medical image analysis, and large deformation registration poses significant challenges due to substantial appearance differences between the fixed image and the moving image. With the advancement of artificial intelligence, reinforcement learning has been gradually incorporated into image registration tasks. Notably, Ziwei Luo, Jing Hu, and other researchers introduced the first reinforcement learning‐based large deformation registration model called Stochastic Planner‐Actor‐Critic (SPAC), achieving superior performance in large deformation registration compared with advanced deep learning registration methods, which is the only large deformation registration reinforcement learning model. However, the existing reinforcement learning model for large deformation registration do not consider the impact of Q‐value prediction accuracy on the overall task results. This study aims to investigate brain large deformation registration under the influence of Alzheimer's disease. To further improve the accuracy of brain large deformation registration, this paper combines the proven advanced Q‐value distribution prediction in reinforcement learning with the large deformation registration model SPAC, designing a distributional SPAC model (DSPAC). Subsequent experiments on the ADNI dataset demonstrate the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Foot pain, particularly caused by heel spurs and Sever's disease, significantly impacts mobility and daily activities for many people. These diseases are traditionally diagnosed by orthopedic specialists using X‐ray images of the lateral foot. In certain situations, the absence of specialists requires the adoption of AI‐based methods; however, the lack of a dataset hinders the use of AI for the preliminary diagnosis of these diseases. Therefore, this study first presents a novel dataset consisting of 3956 annotated lateral foot X‐ray images and uses the original capsule network (CapsNet) to automatically detect and classify heel bone diseases. The low accuracy of 73.99% of CapsNet due to the low extraction feature layers led us to search for a new model. For this reason, this paper also proposes a new enhanced capsule network (HeCapsNet) by adjusting the features extraction layers, adding extra convolutional layers, using "he normal" kernel initializer instead of "normal" and utilizing the "same" padding scheme to perform better with medical images. Evaluating the performance of the proposed model, higher accuracy rates are achieved, including 97.29% for balanced data, 94.19% for imbalanced data, area under the curve (AUC) of 98.69%, and a fivefold cross‐validation accuracy of 95.77%. We then compared our proposed model with state‐of‐the‐art modified CapsNet models using various datasets (MNIST, Fashion‐MNIST, CIFAR10, and brain tumor). HeCapsNet performed similarly to modified CapsNets on relatively simple non‐medical datasets such as MNIST and Fashion‐MNIST, but performed better on more complex medical datasets. [ABSTRACT FROM AUTHOR]

Artificial neural network‐based computing is anticipated to surpass the von Neumann bottleneck of traditional computers, thus dramatically boosting computational efficiency and showing a wide range of promising applications. In this paper, sol−gel deposition was used to prepare thin films of samarium‐doped hafnium dioxide (Sm:HfO2). When Sm is doped at a concentration of 4%, it mimics biological synapses; meantime, by voltage scanning, an obvious mimicry of resistive switching can be detected, demonstrating that the technology may be applied to simulate biological synapse characteristics, including long‐term potentiation (depression), short‐term potentiation (depression), paired‐pulse facilitation, and learning rules of spike‐time‐dependent plasticity. Additionally, a pulsed neural network is built on the MNIST dataset to test the memristor's capacity to handle visual input. The findings show the possibility of synthetic synapses in artificial intelligence systems that integrate neuromorphic computing with synaptic brain activity. [ABSTRACT FROM AUTHOR]

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

This study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI-based chat model, in the field of radiology. The primary focus is on its role in enhancing decision-making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision-making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board-style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision-making and optimizing workflow. While ChatGPT’s promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology. [ABSTRACT FROM AUTHOR]

Background: The increasing prevalence of Artificial Intelligence (AI) language models, exemplified by ChatGPT, has sparked inquiries into their influence on creative writing skills in educational contexts. This study aims to quantitatively investigate whether ChatGPT's use negatively affects university students' creative writing abilities, focusing on originality, content presentation, accuracy, and elaboration in essays. The research adopts an experimental approach to shed light on this concern. Objective: This study aims to quantitatively investigate whether the utilization of ChatGPT, an AI chatbot, adversely affects specific dimensions of creative writing skills among university students, with an emphasis on originality, content presentation, accuracy, and elaboration. Method: The experimental study involves 600 students from 10 universities, divided into a control and an experimental group (EGp). The EGp incorporates ChatGPT in their creative writing process as an intervention. The study evaluates originality, content presentation, accuracy, and elaboration, utilizing the Wilcoxon Signed‐Rank Test for analysis. Results and Conclusion: The findings reveal a detrimental association between ChatGPT use and university students' creative writing abilities. Analysing both machine‐based and human‐based assessments substantiates earlier qualitative observations regarding ChatGPT's adverse impact on creative writing. This study highlights the necessity of approaching AI integration, particularly in creative writing disciplines, with caution. While AI tools have merits, their integration should be thoughtful, considering the potential drawbacks. These insights inform future research and educational practices, guiding the effective incorporation of AI while nurturing students' writing skills. Lay Description: What is already known about this topic: ChatGPT poses an ethical dilemma regarding its use in the field of academiaQualitative claims and opinions have been raised in prior studies regarding its use in the creative writing processPrior studies have both supported and opposed its use but with very limited quantitative approaches while most of the opinions remain qualitativeSome prior studies opine in support of ChatGPT's ability as an authorSeveral factors measuring creativity has been identified by previous studies but a constructive approach in the light of advanced Artificial Intelligence (AI) based chatbots like ChatGPT is missing in such literature What this paper adds: An experimental approach to provide a valid quantitative proof of the qualitative claims over ChatGPT's detrimental effect towards creativity in writing, which was absent in prior studiesA multifactor‐based formula to measure creativity in a quantitative formA quantitative view of the factors that are affected in either a positive way or a negative way in a user by ChatGPT, providing a holistic picture to determine its extent of useA statistical and theoretical understanding over an unexplored topic like creative writing in the light of ChatGPTA quantitative proof why ChatGPT should not be considered as an author Implications for practice and/or policy: Educators may implement changes in assigning tasks to students compared to their earlier practices, based on the identified factors that are being affected negatively, to ensure ChatGPT does not hinder a student's creativity at a greater extentThe extent of using ChatGPT should be limited to self‐learning as positive effect was experienced through the experimentPolicymakers may use the findings of the study to impose strict policies in academia for ensuring academic integrity (Example: must use of plagiarism detecting software for checking scripts, assigning tasks to students which require more analytical abilities, providing tasks which are not properly readable by LLM's like ChatGPT such as image‐based questions, case studies etc.) [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by investigating how children were engaged in AI literacy activities that are supported by intelligent agents. These activities were implemented in a Hong Kong kindergarten with a class of six 5‐year‐olds (Mage = 62.83 months, SD = 2.91) over a 6‐week period. Methods: The study gathered data from multiple sources, including classroom observations, teacher interviews, and documents/artefacts. Results and Conclusions: The results showed that children could learn about AI through interaction with intelligent agents in embodied learning contexts. The findings of this study have implications for the broader field of digital technology education, particularly in the context of early childhood education. Lay Description: What is already known about this topic: Artificial Intelligence (AI) literacy is essential in today's world.Little research exists on engaging young children in learning AI literacy. What this paper adds: Technology‐enhanced embodied learning approach affords multimodality in enabling young children's learning with AI‐related agents.Project‐based learning and real‐life examples can enhance children's learning. Implications for practice and/or policy: Early exposure to AI can help children navigate the increasingly digital world.Practical approaches, such as embodied and multimodal learning, seem to be effective.Teachers can enhance children's digital learning by using pedagogical strategies like project‐based learning and real‐life examples. [ABSTRACT FROM AUTHOR]

Deep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type of deep generative models, called conditional Generative Adversarial Networks (cGAN), has been used since a few years in the context of image‐to‐image (I2I) translation on solar observations. These algorithms have however hyperparameters whose values might influence the quality of the synthetic image. In this work, we use magnetograms produced by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and EUV images from the Atmospheric Imaging Assembly (AIA) for the problem of generating Artificial Intelligence (AI) synthetic magnetograms from multiple SDO/AIA channels using cGAN, and more precisely the Pix2PixCC algorithm. We perform a systematic study of the most important hyperparameters to investigate which hyperparameter might generate magnetograms of highest quality with respect to the Structural Similarity Index. We propose a structured way to perform training with various hyperparameter values, and provide diagnostic and visualization tools of the generated versus targeted image. Our results shows that when using a larger number of filters in the convolution blocks of the cGAN, the fine details in the generated magnetogram are better reconstructed. Adding several input channels besides the 304 Å channel does not improve the quality of generated magnetogram, but the hyperparameters controlling the relative importance of the different loss functions in the optimization process have an influence on the quality of the results. Plain Language Summary: Performance of space weather forecasting methods relies on the availability of data to be ingested in physical models, which is scarce in space weather as compared to terrestrial weather. Since a few years, deep learning methods have produced algorithms capable of performing image‐to‐image‐translation that are used in solar physics to mitigate this scarcity issue. In this work, we consider a deep generative model called the Pix2PixCC algorithm, which is based on conditional Generative Adversarial Networks. One needs to fix some hyperparameters in the Pix2PixCC algorithm, such as for example, the number of filters used to build the features that characterize the information contained in the data. Our aim in this paper is to make an extensive study of these hyperparameters. We propose a structured way to perform training with various hyperparameter values and provide diagnostic and visualization tools of the generated versus real image. Our results show that a higher number of filters will improve the quality of the synthetic magnetogram and that the respective contribution of each loss function in the global objective function to be minimized also has an influence on the quality of the result. Key Points: We study an image to image translation algorithm that generates solar vector magnetograms from multichannel extreme ultraviolet observationsWe provide insight into the architecture and optimization of the Pix2PixCC algorithm, a conditional Generative Adversarial NetworkOur experimental design highlights relevant hyperparameters, and our interactive tool analyses the quality of the generated magnetograms [ABSTRACT FROM AUTHOR]

The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved. [ABSTRACT FROM AUTHOR]

Android malware has been emerged as a significant threat, which includes exposure of confidential information, misrepresentation of facts and execution of applications without the knowledge of the users. Malware analysis plays an essential role in dealing with the unlawful behaviour of such malicious applications. Android malware analysis involves examining and understanding malware behaviour and its characteristics. It also includes potential adversarial impacts on Android devices. This paper presents a quick understanding and a holistic view of malware detection and analysis. The current investigation conducted a systematic literature review (SLR) to recognize the salient shifts in malware detection by examining a range of scholarly journals and conference papers. The SLR investigated 99 articles published between the years 2018 and 2023. The key observation of this SLR is that static analysis is the most implemented approach for detecting Android malware; Apktool and Androguard are the most frequently used tools. This study also conceded that deep learning and machine learning models have more potential to analyse the malicious behaviour of malware. Certain challenges are faced in Android malware analysis, that is, obfuscation techniques, dynamic code loading, and issues related to experimented datasets. Further, this study focuses on the following areas: the definition of the sample set, data optimisation and processing, feature extraction, machine learning application, and classifier validation. This investigation differs from previous analyses of Android malware detection by emphasizing additional methods based on 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]

With the rapid development of big data science, the research paradigm in the field of geosciences has also begun to shift to big data‐driven scientific discovery. Researchers need to read a huge amount of literature to locate, extract and aggregate relevant results and data that are published and stored in PDF format for building a scientific database to support the big data‐driven discovery. In this paper, based on the findings of a study about how geoscientists annotate literature and extract and aggregate data, we proposed GeoDeepShovel, a publicly available AI‐assisted data extraction system to support their needs. GeoDeepShovel leverages state‐of‐the‐art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc., in a human–AI collaboration manner. As a part of the Deep‐Time Digital Earth (DDE) program, GeoDeepShovel has been deployed for 8 months, and there are already 400 users from 44 geoscience research teams within the DDE program using it to construct scientific databases on a daily basis, and more than 240 projects and 50,000 documents have been processed for building scientific databases. [ABSTRACT FROM AUTHOR]

This paper aims to explore the possibility of employing large language models (LLMs) – a type of artificial intelligence (AI) – in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation and potential risk reduction measures are analysed. The existing literature is reviewed to investigate the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the OECD.ai platform was used. The review highlights the dual‐use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework. The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual‐use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability and safety. Additionally, AI's potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks. [ABSTRACT FROM AUTHOR]

Data extraction is a time‐consuming and resource‐intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently. [ABSTRACT FROM AUTHOR]

Parametric modeling and generative deep learning for bridge design Given the significant environmental impact of the construction industry, the analysis and, above all, optimization of the sustainability of structures while maintaining established levels of reliability are becoming increasingly important. While there are initial tools for life cycle analysis in the building sector, these are not directly transferable to bridge structures. This paper introduces a deep learning‐based approach to explore and model the design space of parametric bridge models and their performance evaluations, illustrating its application for multi‐objective optimization of reinforced concrete frame bridges. Initially, data is synthetically generated using a parametric bridge model and coupling analysis software, followed by training a conditional variational autoencoder (CVAE) as a metamodel. In the context of conceptual bridge design, the CVAE serves as an efficient co‐pilot for both forward and inverse analysis. The sensitivity analysis performed with the CVAE reveals relationships between design parameters and/or performance metrics, highlighting optimization potentials. The integrated framework presented here has the potential to realize efficient bridge design, particularly focusing on sustainability and structural safety criteria, and can be easily extended to other parametric inquiries. [ABSTRACT FROM AUTHOR]

Image recognition methods for the partial inspection of fire safety equipment – a study considering machine learning techniques and the integration of expert knowledge This paper explores the potential application of artificial intelligence (AI) and computer vision in the context of Building Information Modeling (BIM) for documenting fire safety equipment. Detection algorithms, particularly current You Only Look Once (YOLO) models, are utilized to automatically identify fire safety equipment such as fire extinguishers, fire blankets, smoke detectors, and fire alarms in images. The results demonstrate high detection accuracy in both images. Future work aims to expand automated detection by directly integrating it into BIM models. In addition, the importance of expert knowledge in determining the extinguishing agent type of a fire extinguisher is addressed to verify and, if necessary, correct the prediction of an AI‐based fire safety inspection. The developed AI applications aim to increase the level of automation in fire safety inspections to improve the safety of buildings in the operational phase. [ABSTRACT FROM AUTHOR]

Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors. Highlights: AI was used for the first time to evaluate fatigue life of shear connectors in bridges.AI predicted fatigue life of shear connectors more accurately than the current methods.GPR and DT algorithms were the best algorithms for assessing fatigue life of connectors.The current equations in AASHTO for fatigue may need to be redeveloped based on AI. [ABSTRACT FROM AUTHOR]

Concerns about runaway artificial intelligence (AI) – including large language models (LLMs) like ChatGPT – are at the forefront of contemporary political, social, and scientific discourse. This commentary provides a first look at ChatGPT's capabilities and limitations in supporting geographic research, critical thinking, learning, and curriculum development. We assessed ChatGPT's geographic knowledge, synthesising abilities, and potential for extrapolation. ChatGPT was employed for writing assistance, research evaluation, curriculum material creation, and content generation. Despite achieving scores of 47% to 55% on an actual exam paper, ChatGPT exhibited shortcomings including the generation of false references. Ethical concerns regarding academic misconduct, model bias, robustness, and toxic output were also identified. We assert that AI and LLMs like ChatGPT have transformative potential in Geography education and knowledge production but demand critical usage. Accordingly, we urge geographers to enhance AI literacy to enable responsible and effective use of these assistive technologies in our academic practice. This commentary provides a first look at ChatGPT's capabilities and limitations in supporting geographic research, critical thinking, learning, and curriculum development. We assessed ChatGPT's geographic knowledge, synthesising abilities, and potential for extrapolation as well as its use as a supportive technology for writing assistance, research evaluation, curriculum material creation, and content generation. We close by urging geographers to enhance AI literacy to enable responsible and effective use of these assistive technologies in our academic practice. [ABSTRACT FROM AUTHOR]

The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

Objective: In community health, there is great demand but limited time for the delivery of staff and patient education. During the COVID‐19 pandemic, evolving needs necessitated that health education be provided in an accurate and timely manner. This paper describes the development, implementation, and evaluation of a YouTube channel designed to disseminate health education to a wide audience of public health workers and patient populations. Methods: This project, divided into three phases, originated within shelter‐based care, providing education to shelter staff (Phase 1) and overtime has evolved to provide education within the Community Health Worker Hub at a major teaching hospital for community health workers (Phase 2) and the populations they serve (Phase 3). Further, during phase 3, the project developer used an artificial intelligence (AI) platform to increase the reach of the YouTube channel. Results: Over a span of 21 months, 18 unique videos have garnered 489 views. Conclusions: Clinicians and educators can leverage technology platforms for health education delivery, increasing the reach of their work while meeting the demands of the profession. While the quality of some information on YouTube may be poor, patients and students turn to this platform for health education. It is imperative that public health nurses embrace this medium, rather than push against it. By creating high‐quality content, educating students and patients about DISCERN and PEMAT tools, and guiding patients to credible sources, public health nurses may ameliorate the standards of health education on YouTube. [ABSTRACT FROM AUTHOR]

Multiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests. [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]

This paper explains the importance and benefits of using AI in fluid mechanics, emphasizing its capability of finding the solution of fluid flow systems through iterative optimization techniques. It explores how AI can enhance the understanding of fluid dynamics to improve engineering processes. In the presented studies, the heat and mass transport of the Casson nanofluidic flow model on a nonlinear slanted extending sheet (HMT‐CNFM) via AI‐based Levenberg Marquard methodology with backpropagated trained neural networks (TNN‐BLMM) is studied. By applying an appropriate transformation, the governing PDEs representing HMT‐CNFM are converted into a system of nonlinear ODEs. The dataset for Levenberg Marquard methodology with backpropagated trained neural network (TNN‐BLMM) for all six scenarios of this proposed model via computational power of the Lobatto IIIA scheme using the "bvp4c" package in MATLAB and then graphically illustrate all these six scenarios through nftool to attain mean square error, regression, error histogram, performance, and fit curve. Training, testing, and validation processes of NN‐BLMM are organized for the investigation of the HMT‐CNFM model. [ABSTRACT FROM AUTHOR]

Software‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%. [ABSTRACT FROM AUTHOR]