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Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR]

Digital transformation rapidly changes how we live our lives in the post pandemic world. Unfortunately, digital technology is not limited to law abiding organisations and citizens. Criminal organisations and individuals are quick to identify new opportunities with new technologies, and digital transformation is dramatically changing the character of crimes, terror, and other threats. The fast emergence of new crimes is facilitated by possibilities brought by disruptive technologies such as AI, Internet of Things, drones, and cryptocurrencies that can be disastrous tools in the hands of criminals. Consequently, our society needs far better capacity to prevent and investigate criminal acts to protect organisations and citizens. This brings an urgent need to proactively reform digital forensics to significantly increase our capability to meet the strain on society brought by crimes evolving in the digital transformation era. The future of forensic science is already here, characterized by a mix of opportunities and challenges. It is essential to make it harder to effectively use digital technologies for criminal activities, while leveraging the possibilities of digital technologies by those affected, law enforcement agencies, business and organisations. As digital technologies continue to evolve, we need to stay up to date with the latest developments to effectively investigate and prosecute crimes in the digital age. There is an increased reliance on digital evidence, and the amount of heterogeneous digital evidence in criminal cases keep increasing. The forensic science techniques thus become more sophisticated and play an increasingly important role. However, the scientific area is extremely broad, and beyond the capability of most forensic science labs to keep up with the technology forefront development speed. Besides an urgent need to bring up the subject to the political arena, examples of how we can meet the challenges are discussed such as by extending our cooperation, encourage and facilitate cooperation for training and education to handle the extremely broad and rapid development, working out methods for explaining and visualising evidence for the treatment and legal values of digital evidence in prosecution, and cooperation between product developers and crime investigators for swift innovation of digital forensics tools and methodologies for quickly emerging threats. This paper will highlight specific examples where modern digital techniques are used to solve crimes in the physical world as well as crimes committed in the digital domain and discuss how "good AI" can be used to fight "evil AI" and finally touch on the sensitive balance between the increased power of the new digital forensic tools and private integrity. Digital transformation is not limited to law abiding organisations and citizens, dramatically changing the character of crimes, terrorism and other threats. The fast emergence of new crimes is facilitated by possibilities brought by disruptive technologies that can be disastrous tools in the hands of criminals. This brings an urgent need to proactively reform digital forensics to meet the strain on society brought by crimes evolving in the digital transformation era. As digital technologies continue to evolve, we need to stay up to date to effectively investigate and prosecute crimes. As the scientific area is extremely broad, and beyond the capability of most forensic science labs to keep up with the technology forefront development speed we need to encourage and facilitate broad cooperation to better handle the rapid development. This paper will highlight the impact of digitalisation in digital forensics, including AI as disruptive technology, with a few examples on how we can combat "evil AI" by using "good AI". [Display omitted] The Digital Forensic Loop. • The evolving landscape of digital forensics and the intersection of digital forensics and the rapid digitalisation. • The impact of digitalisation in digital forensics, including AI as disruptive technology, with a few examples on how we can combat "evil AI" by using "good AI". • Digital Forensics Sweden DFS - An example of a national collaborative network on Digital Forensics. • Use of Digital Twins in forensic investigations. [ABSTRACT FROM AUTHOR]

This paper analyzes the evolution of Artificial Intelligence (AI) in the construction industry from 2014 to 2023, focusing on enhancing security, communication, and maintenance. It combines in-depth analysis of 121 papers with visualizations of 507 articles from major databases such as SCOPUS, IEEE, ACM, Science Direct, and Google Scholar to map AI advancements in construction. The study found that security is established as a mature research domain, whereas communication and maintenance are at comparatively earlier stages of development. Specifically, the analysis reveals a shift from Radio Frequency Identification (RFID) to more sophisticated technologies such as Internet of Things (IoT), Virtual Reality (VR), blockchain, Building Information Modeling (BIM), and digital twins, which significantly improve security. Communication and maintenance have also evolved towards greater digital integration and predictive analytics. The integration of AI innovations with human expertise is emphasized as a strategic direction to enhance decision-making and operational efficiency in construction. [Display omitted] • Review the development of AI-driven studies in the construction sector. • AI evolution in construction security, communication and maintenance (2014–2023). • AI shifts to IoT, VR, blockchain, BIM, digital twins. • Challenges in data management, AI integration noted. • Predictive analytics, ethical AI use in future directions. [ABSTRACT FROM AUTHOR]

This comprehensive review paper investigates the possibilities and prospects of operation and maintenance (O&M) technologies in small modular reactors (SMRs). The necessity for innovation within the regulatory restrictions is a primary concern. Artificial intelligence, digital twins, and robotics are emphasized as promising technologies for autonomous operation, real-time monitoring, predictive maintenance, and maintenance automation, respectively. The paper argues that these technologies can improve safety, increase economic efficiency, and enhance the competitiveness of SMRs. In addition, the paper proposes a methodology for performance verification that employs separate effect tests (SETs) and integral effect tests (IETs) facilities. A realistic roadmap is also provided, suggesting a six-year timeframe for the implementation and licensing of these technologies. By combining innovative technologies and regulatory considerations, the study proposes a future direction for SMRs' operation and maintenance. The final objective of this work is to provide insights into the current status of SMRs' O&M technologies, to suggest potential paths forward, and to make SMRs a more competitive choice in the global energy market. The conclusions drawn from this review can serve as a valuable guide for researchers, plant operators, and policymakers in the nuclear energy sector. • There is an economic issue with small modular reactors (SMRs). • Currently, reducing operation and maintenance (O&M) costs is essential. • A comprehensive review has been conducted regarding regulations and technology. • Presents a realistically achievable cost reduction strategy for SMRs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Blockchain and AI integration enhances healthcare security and efficiency. • Synergy aims for a secure healthcare metaverse via blockchain–AI. • Addressing technical and privacy issues is vital for integration success. • Future focus on setting interoperability standards and privacy investments. • Joint research and teamwork are essential for blockchain–AI advancements. This perspective paper explores the synergistic potential of blockchain and artificial intelligence (AI) in transforming healthcare. It begins with an overview of blockchain's role in healthcare data management, security, the pharmaceutical supply chain, clinical trials, and health insurance. The discussion then shifts to the impact of AI on healthcare, followed by an examination of integrated AI–blockchain platforms and their benefits. Technical challenges, limitations, and solutions related to these technologies are scrutinized. The paper addresses regulatory compliance and ethical considerations, and proposes future directions for their implementation. It concludes with research and implementation guidelines, offering a roadmap for harnessing blockchain and AI to enhance healthcare outcomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence has advanced the applications of sensor-based human motion capture and recognition technology in various engineering fields, such as human–robot collaboration and health monitoring. Deep learning methods can achieve satisfactory recognition results when provided with sufficient labeled data. However, labeling a large dataset is expensive and time-consuming in practical applications. To address this issue, this paper proposes a deep convolutional transformer-based contrastive self-supervised (DCTCSS) model under the bootstrap your own latent (BYOL) framework. The DCTCSS model aims to achieve reliable activity recognition using only a small amount of labeled data. Firstly, a deep convolutional transformer (DCT) model is proposed as the backbone of DCTCSS model, to learn high-level feature representations from unlabeled data in pre-training period. Subsequently, a simple linear classifier is trained with supervised fine-tuning using a limited amount of labeled data to recognize activities. In addition, this paper experimentally formulates a random data augmentation strategy to increase the diversity of input data. The performance of the DCTCSS model is evaluated and compared with several state-of-the-art algorithms on three datasets widely used in daily life, medical monitoring, and intelligent manufacturing. Experimental results show that the DCTCSS model achieves mean F1 scores of 95.64%, 88.39%, and 98.40% on the UCI-HAR, Skoda, and Mhealth datasets, respectively, using only 10% of the labeled data. These results outperform both supervised and unsupervised baseline models. Consequently, the DCTCSS model demonstrates its effectiveness in reducing the dependence on large amounts of labeled data while still achieving competitive activity recognition performance. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in scopes, goals, and methodologies, some difficulties have gradually emerged, including some vague terminology, ambiguous views, and scattered research. Hence, a systematic and comprehensive review of the development process and advanced research about CCL is particularly necessary at the moment. This paper summarizes the theoretical significance, application value, and future development potential of CCL. More importantly, by synthesizing the reviewed related research, we can acquire some interesting results and answer three essential questions: (1) why examine a cognitive and learning framework based on concept? (2) what is the concept-cognitive learning? (3) how to make concept-cognitive learning? The findings of this work could act as a valuable guide for related studies in quest of a clear understanding of the closely related research issues around concept-cognitive learning. • It is the first paper that attempts to provide an in-depth analysis of the advancement of CCL. • It is a multi-view categorization of CCL in research scopes, goals, and methodologies. • It is an elucidation of the main research gaps and suggestions for the related study of CCL. • It acquires some interesting results by synthesizing the reviewed related research of CCL. [ABSTRACT FROM AUTHOR]

• Examine existing literature on applying AI in the modular construction industry. • Exploring the production, operations and logistics (POL) management. • Analyze the progress and research patterns of AI for POL management. • Provide potential future directions for AI for POL management. Artificial intelligence (AI) has garnered significant attention within the modular construction industry, emerging as a prominent frontier development trend. A comprehensive and systematic analysis is required to gain a thorough understanding of the existing literature on the use of AI in the management of production, operations, and logistics within the modular construction industry. This review delves into the various aspects of AI implementation in this sector, adopting a critical perspective. The objective of this paper is to analyze the progress, suitability, and research patterns in the field of AI for the management of productions, operations, and logistics within the modular construction industry. First, a concise overview of AI technologies pertaining to the contemporary research on the production, operations and logistics management of the modular construction industry is provided. Second, a bibliometric analysis is performed to provide a comprehensive overview of the existing publications pertaining to this subject matter. Subsequently, this paper presents literature reviews and outlines future directions for each component, specifically AI in the context of production management, operations management, and logistics management within the modular construction industry. The review provides a valuable knowledge base and roadmap to guide future research and development efforts in AI-enhanced modular construction management. [ABSTRACT FROM AUTHOR]

• This paper proposes a ship safety risk early warning model integrating transfer learning and multi-modal learning. • This model uses the transfer learning method to verify whether the data capabilities one area can be effectively applied in another area. • This model can alleviate the management pressure of maritime regulatory institutions. • This model can enhance the safety of the ship. An efficient risk warning model is crucial to the navigation safety of ships. However, existing researches are often limited by data acquisition, resulting in model training limited to specific sea areas, and data transferability among different sea areas has not been fully explored. Therefore, this paper studies the feasibility of some artificial intelligence methods in risk warning and the transferability of pre-trained models among different regions based on sizeable historical weather data sets, thus proposing a ship risk early warning model integrating transfer learning and multi-modal learning. This model integrates ship traffic flow data, weather data, ship accident data, ship parameters and other related data, and uses machine learning algorithms to pre-train the model to analyze possible risks to ship navigation safety driven by high-dimensional data. And discuss the generalization and transferability of the model based on transfer learning methods. Research results show that the ship navigation risk early warning model that integrates transfer learning and multi-modal learning can effectively identify ship navigation risks and has high accuracy in some transfer sea areas. This innovation has important practical significance in ship safety, improving global ship safety levels and providing more comprehensive and reliable decision-making support for risk management in the shipping industry. [ABSTRACT FROM AUTHOR]

Technological disruptions, health crises, persistent labour issues, and demographic shifts have elevated the importance of RAISA (robots, artificial intelligence, and service automation), and Generation Z for hospitality organisations. This study reviews 81 peer-reviewed articles using bibliometrics, quantitative frequency, and qualitative thematic analysis to examine Generation Z's engagement with RAISA in hospitality services. The findings highlight the increasing prevalence of service robots in hospitality sectors, with other AI and smart technologies remaining underexplored. While prior studies focused on Generation Z consumers' RAISA usage intentions, they overlooked the perspectives of Generation Z employees and hospitality operators. Furthermore, prior studies were mostly quantitative and geographically constrained, and focused on examining existing technology acceptance models, thereby lacking cross-cultural, in-depth, and mixed-methods exploration. Accordingly, this review paper proposes future research directions, and a novel service encounter paradigm. The paper also suggests hospitality operators proactively adopt RAISA technologies and prioritise Generation Z to aid sustainable operations. • Mixed-methods, theory-informed systematic review on Gen Z's engagement with RAISA technologies in hospitality services. • Examines all facets of RAISA by acknowledging their interdependence and compatibility in providing hospitality services. • Suggests cross-cultural, mixed-methods studies on Gen Zs' responses to RAISA technologies beyond behavioural intention. • Proposes a novel service paradigm based on Bateson's Service Encounter Triad for contemporary hospitality services. [ABSTRACT FROM AUTHOR]

This paper explores the effects of integrating Generative Artificial Intelligence (GAI) into decision-making processes within organizations, employing a quasi-experimental pretest-posttest design. The study examines the synergistic interaction between Human Intelligence (HI) and GAI across four group decision-making scenarios within three global organizations renowned for their cutting-edge operational techniques. The research progresses through several phases: identifying research problems, collecting baseline data on decision-making, implementing AI interventions, and evaluating the outcomes post-intervention to identify shifts in performance. The results demonstrate that GAI effectively reduces human cognitive burdens and mitigates heuristic biases by offering data-driven support and predictive analytics, grounded in System 2 reasoning. This is particularly valuable in complex situations characterized by unfamiliarity and information overload, where intuitive, System 1 thinking is less effective. However, the study also uncovers challenges related to GAI integration, such as potential over-reliance on technology, intrinsic biases particularly 'out-of-the-box' thinking without contextual creativity. To address these issues, this paper proposes an innovative strategic framework for HI-GAI collaboration that emphasizes transparency, accountability, and inclusiveness. • Generative AI (GAI) excels in unfamiliarity and info-overload situations, whereas intuitive System 1 in human intelligence (HI) is less effective. • GAI reduces heuristic biases and cognitive burdens, enhancing decision quality through System 2 reasoning. • Challenges include over-reliance on GAI and the need for contextual creativity to support 'out-of-the-box' thinking. • Strategic HI-GAI collaboration, with GAI handling data-intensive tasks and HI using intuition for contextual tasks. • Proposes a framework for HI-GAI collaboration, emphasizing transparency, accountability, and inclusiveness. [ABSTRACT FROM AUTHOR]

The interpretability of machine learning (ML) and neural networks has been attached great importance in medical-related applications. Considering random forest (RF) algorithm with self-explanatory function then integrating the concept of human–machine hybrid system, this paper proposes a expert-involved explainable artificial intelligence (XAI) framework for essential tremor (ET) action recognition, which assists to reveal the black-box model globally and locally, and to provide decision-making suggestions for medical practitioners. In addition, the human empirical judgment of clinical diagnosis is integrated into the action recognition process, which contributes to the model performance improvement and forms a dynamic and feedback loop. This paper collecting sensing data of six actions from 25 participants (20 ET patients and 5 healthy subjects) via smart phone monitoring system. Standardization, feature extraction and data segmentation are used to select the key features. The RF is used to train and test datasets with three different dimensions, and the six-dimensional dataset is selected for interpretable analysis, in which the expert rating data improve the recognition performance. Then, the feature importance, permutation importance, and Shapley additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) methods are applied from abstract to concrete for model interpretability. The model-agnostic explainable approach are relatively flexible and universal, which can visualize the features that have positive or negative impacts on the action recognition process respectively, and obtain the specific abnormal value for further diagnosis assistance. The proposed XAI framework is expected to realize the analysis of a single abnormal sample and provide more acceptable and reliable empirical suggestions for early diagnosis and healthcare. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is emerging as a promising healthcare technology. Especially in critical, data-driven, and complex environments such as acute care, the use of AI can significantly improve treatment processes and support clinical staff. To date, AI applications in healthcare are scarce. Research remains fragmented across individual applications. To address this gap, we conduct a systematic literature review. In this review, we map the status quo of AI research in acute care and use service-dominant logic (SDL) from service science as a framework to integrate our analysis. Using a multilayered lens, we (1) identify intended beneficiaries of AI, (2) identify relevant activities supported by AI, and (3) determine those steps of the patient journey that have been in the spotlight for AI research. Our findings suggest that researchers have focused on hospital staff members as intended beneficiaries during the first three steps of the patient journey: admission, diagnosis, and treatment. The patient's perspective, however, remains underexplored. Moreover, 96 % of the research papers we reviewed focus on AI development and proof-of-concept studies, while only 4 % employ and test AI applications in the field. We identify three priorities for future AI research in acute care and provide suitable research methods. • First approach to combine systematic literature review with key determinants of SDL • SDL as an appropriate means to assess existent AI research in acute care • Four analysis determinants (micro level, meso level, activities, patient journey) • 96 % of the research papers focus on AI development and proof-of-concept. • 4 % of the research papers employ field study testing. [ABSTRACT FROM AUTHOR]

Role-playing games (RPGs) are interactive gaming experiences designed and driven by the intricate development of narratives and characters. However, these games also highlight elements, such as combat, exploration, creativity, and crafting. Game developers helped to shape the market by incorporating RPGs into digital media; however, they suffered restrictions in their game design and storytelling caused by technological limitations. Fortunately, academia has studied these challenges and mitigated these latter issues to better understand and connect digital RPGs with their analog versions. Our research offers a comprehensive overview of studies centered on technologies for managing, generating, and controlling digital RPG elements. We aim to characterize Artificial Intelligence research within this domain and elucidate the diverse techniques employed. In this context, we examined 72 papers about Procedural Content Generation (PCG) in RPG, which were identified by mixing a database search with a snowballing method. In this paper, we provide an overview of PCG techniques investigated within the realm of RPGs and present the trending approaches for future developments in the field. • We present role-playing games and a taxonomy that covers their subgenres and their limits. • We explore works with technologies supporting RPG management and control. • We present the relationship between technologies and content control categories. [ABSTRACT FROM AUTHOR]

The early detection of fuel leaks in filling stations is crucial to minimize environmental risks, such as soil and groundwater contamination. There are some commercial products for fuel leakage detection based on statistical analysis of data from station inventory books. Although they solve the tackled problem, they have some important drawbacks, such as their high price, and issues related to the privacy of station data, which must be shared with the company owning the reconciliation technology. In this work, a solution based on Artificial Intelligence is proposed to address this problem. Machine Learning techniques, specifically two-class supervised classifiers, are applied to data extracted from inventory books of real petrol stations. The classification models used in this paper are trained and tested with real data of days without leaks and simulated data of days with leaks. Thus, the more representative of reality these data are, the better the classifiers will work when implemented in a real filling station. In this sense, the most novel contribution of this paper is the way in which the training sets are constructed to achieve a realistic scenario. These sets are composed of time data windows in which the leak can begin on any day within the window, not necessarily on the first day, as the authors had assumed in a previous contribution. Therefore, they are mixed windows containing a variable number of non-leaking and leaking days. In addition, the design of these data sets complies with the requirements of the current European standard UNE-EN 13160–5. This allows the classifiers to work under even more realistic conditions and thus increase the practical applicability of their results. This work demonstrates that by using two-class classifiers it is possible not only to comply with the standard in terms of the maximum allowable ratio of false positives and false negatives, but also to detect the leak in a shorter time than that established in the norm. [Display omitted] • The detection of fuel leaks in petrol stations is approached as a classification problem. • In order to study compliance with current regulations it is essential to work in a scenario that is as realistic as possible. • Mixed time windows improve the reliability of the results regarding fuel leaks detection. • The proposed solution allows to accomplish the efficiency requirements of current regulations. [ABSTRACT FROM AUTHOR]

Recently, the widespread adoption of artificial intelligence, particularly generative AI technology, has surged across various industries. However, a notable drawback of this technology is its significant energy consumption during model training and operation, which poses challenges to sustainability goals and the environment. Consequently, various initiatives have emerged to promote what is termed "green artificial intelligence," aiming to mitigate these environmental impacts. Nevertheless, research discussing these initiatives remains scarce. Hence, this study aims to identify green artificial intelligence initiatives that contribute to environmental friendliness. This paper has comprehensively reviewed the existing literature, professional websites, and expert blogs to identify and analyze available green AI initiatives. This paper has identified 55 such initiatives, broadly categorized into six themes: cloud optimization, model efficiency, carbon footprinting, sustainability-focused AI development, open-source initiatives, and green AI research and community. This study discusses the strengths and limitations of each initiative to offer a comprehensive overview. The findings provide valuable insights, particularly for industries interested in green artificial intelligence and green technology in general. While some tools have been recognized and studied, comprehensive research and analysis are still required to empirically evaluate the majority of other tools due to their early stages of development in this field. • Study explores the growing awareness and concern about environmental impact of AI. • It investigates development of tools and approaches to address Green AI. • Significance of collaboration, open-source initiatives, in Green AI development. • Examine importance of ethical considerations in green AI development. • Provide insights for industries in green AI and green technology in general. [ABSTRACT FROM AUTHOR]

As the core component of ejector refrigeration systems, the research of ejector mechanisms has always been a hot topic. Based on the compound-choking theory, this paper studies the relationship between the entrainment ratio and critical back pressure and designs a neural network-based online error compensation mechanism for supersonic ejectors. Firstly, a thermodynamic model of the ejector is developed by combining the ideal gas model, the compound-choking theory, and the constant-pressure mixing theory. Then, the thermodynamic relationship between the entrainment ratio and critical back pressure is derived by introducing the idea of a hypothetical mixing cross section. Compared with the existing results, our method is able to improve critical back pressure prediction accuracy by virtue of the actual entrainment ratio. Therefore, a new adaptive error compensation algorithm is proposed for supersonic ejectors based on neural networks. Thus, the proposed scheme can be regarded as a hybrid modeling method for supersonic ejectors, which combines thermodynamic laws and data-driven artificial intelligence algorithms. Experimental data from ejector refrigeration systems with R141b, R245fa, water vapor and R134a are used in this paper to verify the validity of the model. The simulation results show that our method can effectively predict the critical back pressure, and all the prediction errors are less than 10%. • A novel hybrid modeling method is proposed for supersonic ejectors based on the compound-choking theory. • The idea of hypothetical mixing cross section is introduced to develop the ejector model. • The predictive error of the thermodynamic model is compensated by using adaptive neural scheme. • The simulation results show that all prediction errors are less than 10%. [ABSTRACT FROM AUTHOR]

Green finance can effectively support industrial upgrading and technology development, such as artificial intelligence (AI). In this paper, we attempt to pursue the possibility of a win-win situation for achieving an eco- and tech-friendly society. By asking where and how we could develop green finance and AI innovation simultaneously, this paper conducts an empirical investigation on the influence of green finance on the development of AI in China from 2011 to 2020. Based on the panel data from the 30 provinces, we introduce spatial measurement, policy effect, heterogeneity, and threshold analyses to present deeper insights into the impact of green finance on AI innovations. Indeed, green finance could promote the progress of AI innovation. China's green finance pilot policy is verified to promote the progress of local AI innovations. Furthermore, the spatial spillover effect and regional heterogeneity are observed as well. The promotion effect is most significant in the western area, where the green finance index is relatively low. Besides, the threshold analysis also considers how to increase the marginal effect of green finance in different areas. Finally, several policy recommendations are proposed, which contribute to providing specific directions for the policymakers to improve AI innovation and achieve sustainable development at the same time. [ABSTRACT FROM AUTHOR]

• Proposes an improved YOLOv5s model which combines the GIoU, Mish, and CBAM modules. • The improved algorithm can effectively detect different-size insulator defects under different environmental backgrounds. • The AP value in detecting insulator defects can be improved by 5 %. As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects. [ABSTRACT FROM AUTHOR]

Data infrastructures are expanding rapidly across African societies, renewing the promise of modernisation, and providing a massive data resource to the dominant tech powers of the world. Google's private undersea cable, named Equiano, landed in west Africa with the intention of igniting data services, smart environments, investment opportunities and jobs. Non-governmental and multilateral agencies are busy supporting African governments in building regulatory frameworks that aim to 'ready' countries for the 4th industrial revolution. Young Africans labour in remodelled shipping containers to annotate data and train algorithms. While future promises pan out in wide-angled, utopian visions, this paper sets out an approach to understand African contexts as sites of heterogeneous experience of data infrastructures that are historically and politically contingent. The paper explores the spatial politics of extraction, surveillance, and exploitation in three examples of the Equiano pipeline, the Artificial Intelligence (AI) Readiness Index, and the AI annotative labour force. We challenge the homogenising discourses of global tech companies and transnational governance institutions in accounting for the geographical histories of colonialism and its afterlives in African societies. We further call for empirical studies that examine the granular multiplicities of data, providing nuanced understandings of AI in and from Africa. [ABSTRACT FROM AUTHOR]

• This paper proposes an intelligent algorithm based on deep learning for the efficient and accurate segmentation, and identification of fractures in ultrasonic imaging well logging images. • Innovatively adopting the concept of transfer learning, intelligent recognition of fractures is achieved based on the Mask R-CNN model. • We addressed the issue of datasets scarcity by artificially synthesizing data to increase both the quantity and diversity of data types. • By conducting a comparative analysis of mainstream deep learning image segmentation algorithms, we have confirmed the superiority and reliability of the algorithm proposed in our study. Ultrasonic imaging well logging is an oil and gas exploration technique that provides visual identification of reservoir fractures. It holds significant importance in accurately predicting production rates and guiding the development of oil and gas resources. However, traditional methods of fracture identification rely on visual inspection by experts and manual extraction which are often time-consuming and labor-intensive. To address the problems and deficiencies of traditional methods, this study establishes a deep learning method based on the idea of transfer learning, namely Mask R-CNN, to achieve intelligent recognition and segmentation of fractures in ultrasonic logging images. The paper selects ultrasonic imaging logging images containing different types of fractures and expands the data through artificial synthesis, obtaining a large amount of high-quality data for model training to learn the semantic features of fractures. The results indicate that the Mask R-CNN model achieves 93.75% recognition accuracy and 92.92% recall rate for fracture identification in well logging images, which benefits from the idea of transfer learning. Moreover, the segmentation pixel accuracy and intersection over union reach 86.75% and 85.96% respectively. Finally, we further validated the recognition and segmentation performance of the Mask R-CNN model by conducting a comparative analysis with mainstream deep learning image segmentation models, including U-net, DeepLabv3+, and YolACT. The Mask R-CNN model based on transfer learning can effectively identify almost all continuous fractures, demonstrating outstanding detection performance. It is believed that this method can replace manual operations and achieve intelligent, accurate, and efficient automatic recognition and segmentation of fractures in ultrasonic logging images. At the same time, It is confident that artificial intelligence models based on transfer learning methods will have better prospects for application in future processes such as geological information extraction, identification, and analysis. [ABSTRACT FROM AUTHOR]

• Parameter calibration for masonry discrete element models is automatised. • Predicting correct failure mode is ensured by including it in the target function. • Three swarm intelligence algorithms are compared. • The calibration time is accelerated by 50–60 % by using a two-step optimisation. This paper addresses the automatic calibration of constitutive parameters of the Discrete Element Method (DEM) models of masonry structures. Three swarm intelligence algorithms were applied, which are efficient in problems characterised by the small number of unknown parameters but high computational demand for the evaluation of the target function. Trust-Based Particle Swarm Optimisation (TBPSO) was chosen due to its excellent convergence rate. It was already tested that TBPSO converged faster than the original Genetic Algorithm and Particle Swarm Optimisation Methods. Therefore, TBPSO was compared with two recent and promising methods, the Marine Predator Algorithm (MPA) and the Golden Eagle Optimiser (GEO). The paper also presents a novel two-step approach, which enhances the performance of the tested optimisation strategies. The methodology is capable to account for and precisely find the mode of failure using Convolutional Neural Networks (CNN). The three optimisation methods and the two-step approach were applied on the DEM model of a quasi-static masonry arch collapse experiment from the literature. Performance of the three algorithms in identifying the appropriate constitutive parameters values of the DEM model were compared. Clear evidence on the advantage of the two-step approach with TBPSO is demonstrated. Remarks about different modelling issues are also included. [ABSTRACT FROM AUTHOR]

Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance. [ABSTRACT FROM AUTHOR]

• Numerical study of turbulent copper–water nanofluid flow in a heat exchanger. • The heat exchanger in power plant was in the form of a tube with a large number of turbulators. • Reduced entropy production by turbulators length and space. • Larger hole diameter decreases thermal (98.7%), frictional (72.5%), and total entropy (98.5%). This paper presents a numerical investigation of turbulent nanofluid flow in a power plant heat exchanger using conical rings. In this paper focus on varying conical ring hole diameters and their impact on key parameters. Utilizing artificial intelligence techniques, the finite element method (FEM), and the RNG k-epsilon model, analyzed pressure drop, thermal entropy, frictional entropy, and total entropy while varying distances between turbulators (80 mm to 180 mm) and turbulator lengths (45 mm to 60 mm). The results show that the smallest turbulator length and diameter, with an inter-turbulator distance of 145 mm, generate the highest frictional entropy (72 % increase). Smaller distances between turbulators with larger turbulator dimensions result in the smallest pressure drop. Increasing hole diameter significantly reduces thermal (98.7 %), frictional (72.5 %), and total entropy (98.5 %). These findings offer insights for optimizing power plant heat exchanger design using nanofluids and conical turbulators. [ABSTRACT FROM AUTHOR]

The growing integration of AI in agriculture necessitates access to diverse data sets for training artificial intelligence (AI) systems. Manufacturers often face challenges due to a perceived shortage of valid data sets, highlighting the need for centralized platforms offering high-quality data. While global AI regulations are yet to be established, existing laws govern data collection, creation, labeling, and processing for AI training in various legal systems. The absence of explicit AI laws prompts a call for platforms supplying data to adhere to relevant regulations, especially in areas where AI controls devices interacting with humans, such as collision warning systems in yard tractors and robots. Compliance is crucial, given the complexity of rules governing data sets featuring people, animals, and buildings. The paper focuses on how to check the compliant data sets in Germany's legal context, focusing on situations in farm yards. People are often visible on such data sets; thus, the data set must preserve the right to informational self-determination and one's image, which is prevalent in many legal systems. Images portraying people typically qualify as personal data, requiring careful anonymization to address privacy concerns. The paper uses artificial neural networks (ANN) to remove images that contradict people's rights on one's image; thus, data sets that comply with data protection laws can be created. It also addresses the lesser-explored issue of the right to one's image in German law. Though AI training may not be impeded, dissemination via digital platforms must adhere to legal boundaries, even if metadata is absent. The paper suggests an AI solution to identify images within legal limits, detect violations, and modify them to create a legally compliant data set respecting the right to one's image. • Discussing legal conform data sets for the agricultural application domain. • Providing a data set of 2694 images covering the categories: – class (I) not legally compliant and – class (II) legally compliant on the right to one's image; cf. Fig. 1. • Identifying the gap in ANN data sets and models for law compliance checks. • Train and benchmark ANN models for checking law compliance – on the right to one's image; – cf. Tab. 1, Tab. 2, and Fig. 3. • Promoting the importance of AI-based law compliance checks for – detecting violations against the law and – developing compliant AI systems in agriculture. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been hailed for its potential to revolutionize teaching and learning practices. Undoubtedly, there has been development, but has it transferred to a new pedagogical trend? Indeed, research shows more tools, software, etc., built through AI, but there is still a limited understanding of its pedagogical impact. This review aims to assess whether AI has indeed led to new pedagogical trends in education using a Human Center AI framework. To accomplish this, a systematic review of research on the pedagogical applications of AI in K-12 contexts was conducted, following the PRISMA guidelines. The review involved an inductive coding analysis of a comprehensive search across WoS, Scopus, and EBSBU. From a pool of 3277 publications spanning 2019 to 2023, 183 papers met the inclusion criteria for detailed analysis. Six categories emerged: Behaviorism, Cognitivism, Constructivism, Social Constructivism, Experiential Learning, and Community of Practices. The findings of this research provide a promising perspective on synthesizing the results based on the pedagogical framework that describes AI implementation. While technological advancements have improved AI capabilities, the application of AI in education largely follows the same principles of previous technologies. This research suggests that the failure to transform education through AI stems from a lack of consideration of Gardner's proposed ninth intelligence type—pedagogical intelligence. Furthermore, this paper offers a critical analysis of the HCAI framework and proposes an adaptation called Pedagogical Centered AI (PCAI) for designing and using AI in K-12 education. Final discussions highlight the implications and future perspectives of AI in educational settings. • The application of AI in education largely follows the same principles of previous technologies. • Systematic Literature Review of the pedagogical applications of AI in K-12 contexts. • Lack of pedagogical foundations that lead the current research of AI in schools. • Critical analysis of AI research across the lens of pedagogical frames. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-based medical devices and digital health technologies, including medical sensors, wearable health trackers, telemedicine, mobile health (mHealth), large language models (LLMs), and digital care twins (DCTs), significantly influence the process of clinical decision support systems (CDSS) in healthcare and medical applications. However, given the complexity of medical decisions, it is crucial that results generated by AI tools not only be correct but also carefully evaluated, understandable, and explainable to end-users, especially clinicians. The lack of interpretability in communicating AI clinical decisions can lead to mistrust among decision-makers and a reluctance to use these technologies. This paper systematically reviews the processes and challenges associated with interpretable machine learning (IML) and explainable artificial intelligence (XAI) within the healthcare and medical domains. Its main goals are to examine the processes of IML and XAI, their related methods, applications, and the implementation challenges they pose in digital health interventions (DHIs), particularly from a quality control perspective, to help understand and improve communication between AI systems and clinicians. The IML process is categorized into pre-processing interpretability, interpretable modeling, and post-processing interpretability. This paper aims to foster a comprehensive understanding of the significance of a robust interpretability approach in clinical decision support systems (CDSS) by reviewing related experimental results. The goal is to provide future researchers with insights for creating clinician-AI tools that are more communicable in healthcare decision support systems and offer a deeper understanding of their challenges. Our research questions, eligibility criteria, and primary goals were proved using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and the PICO (population, intervention, control, and outcomes) method. We systematically searched PubMed, Scopus, and Web of Science databases using sensitive and specific search strings. Subsequently, duplicate papers were removed using EndNote and Covidence. A two-phase selection process was then carried out on Covidence, starting with screening by title and abstract, followed by a full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) was used to assess the quality and risk of bias. Finally, a standardized data extraction tool was employed for reliable data mining. The searches yielded 2,241 records, from which 555 duplicate papers were removed. During the title and abstract screening step, 958 papers were excluded, and the full-text review step excluded 482 studies. Subsequently, in quality and risk of bias assessment, 172 papers were removed. 74 publications were selected for data extraction, which formed 10 insightful reviews and 64 related experimental studies. The paper provides general definitions of explainable artificial intelligence (XAI) in the medical domain and introduces a framework for interpretability in clinical decision support systems structured across three levels. It explores XAI-related health applications within each tier of this framework, underpinned by a review of related experimental findings. Furthermore, the paper engages in a detailed discussion of quality assessment tools for evaluating XAI in intelligent health systems. It also presents a step-by-step roadmap for implementing XAI in clinical settings. To direct future research toward bridging current gaps, the paper examines the importance of XAI models from various angles and acknowledges their limitations. [ABSTRACT FROM AUTHOR]

Digital Twins (DT) are the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the untapped potential of DT in disaster risk management, and accordingly, proposes an improved Digital twin-based post-disaster risk management framework. This paper employs a systematic literature review approach focusing on digital post-disaster risk management twinning (DPRMT) and smart early warning systems derived from two databases: Scopus and Web of Science. After a screening process with exclusion criteria, the final analysis synthesizes findings from a selected set of 96 papers. The results revealed that previous studies are not beyond only providing general statements about DT. There is a need for diverse data collection methods, considering demographic and financial aspects, understanding social dynamics, employing dynamic models, recognizing interconnected systems, and giving due attention to the often-neglected recovery phase. This study proposes a comprehensive DPRMT concept framework leveraging decision-makers with a holistic and efficient approach that offers real-time, detailed, and data-driven modeling solutions to achieve insights into disaster-affected areas and communities. It is also helpful to optimize response planning, resource allocation, and scenario testing by capturing the dynamic and complex human behaviors and understanding interconnected systems and entities that are often overlooked in previous disaster risk management studies. [ABSTRACT FROM AUTHOR]

This study explores the role of Artificial Intelligence (AI) orientation in facilitating the internationalization speed of enterprises from emerging economies. Based upon a knowledge management perspective, the paper proposes that AI orientation positively affects internationalization speed via broadening the scope of new knowledge, promoting knowledge creation, application, and dissemination within organizations, and hastening the redundancy of existing knowledge to increase adaptability to global dynamics. Moreover, while online local knowledge search facilitates, offline local knowledge search constrains the acceleration effect of AI orientation on internationalization. Using a dataset from China, the paper finds that AI orientation has a significant positive effect on accelerating the internationalization processes of emerging market firms, and home country embeddedness negatively while regional digital development positively moderates this relationship. The findings contribute to the literature by elucidating how AI orientation expedites the process of an emerging market firm's internationalization from a knowledge management perspective, thereby providing insights for the future scholarly investigation and practical applications in this field. • AI orientation expedites internationalization speed. • Offline local knowledge search negatively affects the acceleration effect of AI orientation on internationalization. • Online local knowledge search positively affects the acceleration effect of AI orientation on internationalization. • A knowledge management perspective of AI orientation and internationalization speed is developed. [ABSTRACT FROM AUTHOR]

AI-tools such as ChatGPT can assist researchers to improve the performance of the research process. This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. The study describes a process how to prompt ChatGPT to assist the scale development of a new construct, using the example of the construct of perceived value of ChatGPT-supported consumer behavior. The paper reports four main empirical studies (US: N = 148; Australia: N = 317; UK: N = 108; Germany: N = 51) that have been employed to validate the newly developed scale. The first study purifies the scale. The following studies confirm the adjusted factorial validity of the reduced scale. Although the empirical data imply a simplification of the initial multi-dimensional scale, the final three-dimensional operationalization is highly reliable and valid. The paper outlines the shortcomings and several critical notes to stimulate more research and discussion in this area. • This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. • The study describes a process how to prompt ChatGPT to assist the scale development of a new construct. • Four empirical studies establish the prognostic validity and the construct validity of the newly developed scale. • The paper outlines the shortcomings and several critical notes to stimulate more research and discussion in this area. [ABSTRACT FROM AUTHOR]

• The world is facing rapid population ageing, compounded by declines in the younger workforce that could offer timely caregiving assistance. • Society is increasingly unable to effectively and sufficiently protect people's health and quality of life as they grow old. • Senior employment technology (SET) is a group of technological innovations that have the potential to alleviate or even eliminate the barriers faced by older individuals in the workforce. • SET can empower older individuals to proactively safeguard their current and future living standards. • This paper discusses the potential of SET in facilitating the continued participation of older individuals in the workforce, and underscores the existing barriers preventing the widespread adoption of these technologies. Rapid population ageing, compounded by declines in the younger workforce that could offer timely caregiving assistance, is undermining society's ability to effectively and sufficiently protect people's health and quality of life as they grow old. This paper explores the promise of senior employment technology, a group of technological innovations that have the potential to alleviate or even eliminate the barriers faced by older individuals in the workforce. By doing so, these technologies empower older individuals to proactively safeguard their current and future living standards. This discussion delves into the potential of senior employment technologies in facilitating the continued participation of older individuals in the workforce and highlights the existing barriers preventing the widespread adoption of these technologies. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

This paper engages in a comprehensive investigation concerning the application of Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial Intelligence, with a specific focus on its implications for cybersecurity. Firstly, the paper gives an overview of xAI techniques and their significance and benefits when applied in cybersecurity. Subsequently, the authors methodically delineate their systematic mapping study, which serves as an investigative tool for discerning the potential trajectory of the field. This strategic methodological framework lets one identify the future research directions and opportunities that underlie the integration of xAI within the realm of Deep Learning, Artificial Intelligence, and cybersecurity, which are described in-depth. Then, the paper brings together all the gathered insights from this extensive investigation and closes with final conclusions. • Application of xAI in Network Intrusion Detection. • Research directions in integrating xAI with cybersecurity. • Guide for developing impactful xAI solutions. [ABSTRACT FROM AUTHOR]

Track geometry is one of the critical indicators of railway tracks' condition which requires continuous monitoring and maintenance over time. In this paper, a novel artificial intelligence (AI) based framework is proposed for railway track geometry inspection using vibration data collected from a dedicated measuring high-speed train. This AI-based anomaly track detection approach consists of two main stages. Firstly, a subset of features that best characterizes the track condition is defined. Several dynamic features from time domain data are extracted and importance scores are assigned to them, to determine the most effective subset for the purpose of track condition monitoring. Secondly, a data-driven based anomaly detection approach is developed to assess and identify track geometrical defects. In this stage, the acceleration responses collected from an in-service train traversing on a healthy track zone are employed as input into a One-Class Support Vector Machine (OCSVM) algorithm. The proposed algorithm defines the anomalies as relative changes to the historical behaviour. A comprehensive dataset from field measurements using a Société Nationale des Chemins de Fer Français (SNCF) Réseau IRIS320 highspeed train is used in this paper to implement the proposed approach. In addition, the impact of using different features and different locations/directions of the sensors on the accuracy of detecting geometrical defects is investigated. It is also shown that the OCSVM approach outperforms other algorithms based on Isolation Forest (IF), Local Outlier Factor (LOF), and Robust Mahalanobis Distance (RMD) in terms of recall, precision, and F1-score. The proposed anomaly detection approach has demonstrated a 12% increase in defect detection accuracy compared to the direct utilization of the raw acceleration response, which can facilitate track monitoring using in-service trains while providing cost-efficient maintenance in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

Manually integrating data of diverse formats and languages is vital to many artificial intelligence applications. However, the task itself remains challenging and time-consuming. This paper highlights the potential of Large Language Models (LLMs) to streamline data extraction and resolution processes. Our approach aims to address the ongoing challenge of integrating heterogeneous data sources, encouraging advancements in the field of data engineering. Applied on the specific use case of learning disorders in higher education, our research demonstrates LLMs' capability to effectively extract data from unstructured sources. It is then further highlighted that LLMs can enhance data integration by providing the ability to resolve entities originating from multiple data sources. Crucially, the paper underscores the necessity of preliminary data modeling decisions to ensure the success of such technological applications. By merging human expertise with LLM-driven automation, this study advocates for the further exploration of semi-autonomous data engineering pipelines. [ABSTRACT FROM AUTHOR]

Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game-theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of Integer Linear Programs that were known to be tractable so far. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

• A bibliometric and content analysis was conducted to comprehensively review and analyze deep learning research in cropland field identification. • The paper discusses the challenges of deep learning-based research on cropland field identification, providing readers with insights and directions for future research in the field • This study fills the gap by providing a systematically summarized review of this research area. The cropland field (CF) is the basic unit of agricultural production and a key element of precision agriculture. High-precision delineations of CF boundaries provide a reliable data foundation for field labor and mechanized operations. In recent years, with the dual advancements in remote sensing satellite technology and artificial intelligence, enabling the extraction of CF information on a wide scale and with high precision, research on CF identification based on deep learning (DL) has emerged as a highly esteemed direction in this field. To comprehend the developmental trends within this field, this study employs bibliometric and content analysis methods to comprehensively review and analyze DL research in the field of CF identification from various perspectives. Initially, 93 relevant literature pieces were retrieved and screened from two databases, the Web of Science Core Collection and the Chinese Science Citation Database, for review. The previous studies underwent quantitative analysis using bibliometric software across five dimensions: publication year, literature type and publication journal, country, author, and keyword. Subsequently, we analyze the current status and trends of employing DL in the field of CF identification from four perspectives: remote sensing data sources, DL models, types of CF extraction results, and sample datasets. Simultaneously, we combed through current publicly available sample datasets and data products that can be referenced to produce sample datasets for CFs. Finally, the challenges and future research focus of DL-based CF identification research are discussed. This paper provides both qualitative and quantitative analyses of research on DL-based CF identification, elucidating the current status, development trends, challenges, and future research focuses. [ABSTRACT FROM AUTHOR]

[Display omitted] • Adaptive features based on data feature is used for noise removal for the first time. • The CCR is proposed for the first time to evaluate the denoising effect. • Obtained a more efficient, versatile and intelligent signal denoising method. • Improve the quality of experimental data, promote the engineering application of AI. The sensor signals collected by the nuclear reactor thermal hydraulic experimental system are mixed with complex noise information. The uncertainty of sensor data directly affects the analysis and evaluation effect of machine learning algorithms on the operating status of the experimental system. Traditional denoising methods have poor adaptability to different types of noisy data, are highly dependent on researchers and have high design cost. This paper proposes a data optimization technology based on a hybrid adaptive real-time denoising (HART) model, which can realize data distribution self-adaptation and algorithm hyperparameter self-optimization according to the characteristics of noisy source data. Through the joint denoising algorithm with Variational Mode Decomposition (VMD) algorithm as the core, the partition denoising of source data is realized. In addition, the practical application of the source data of thermal hydraulic experiments in the nuclear field and the verification of simulation and noise addition experiments have been completed. The results show that the denoising optimization model proposed in this paper has the characteristics of good adaptability, self-optimization and real-time processing for different sensor signals. It can effectively remove noise information, restrain data uncertainty, and provide high-quality data source for experimental data analysis based on machine learning. At the same time, the optimization technology can be further applied to the optimization of sensor data of nuclear power plants (NPP). [ABSTRACT FROM AUTHOR]

Intelligent applications are highly susceptible to uncertainty and inconsistency due to the intense and intricate interactions among their autonomous components (or agents), making their verification theoretically and practically challenging. This paper presents the design and implementation of a new open-source and scalable software tool for modeling and verifying intelligent applications with commitment and trust protocols under both uncertainty and inconsistency settings, using reduction-based multi-valued model checking techniques. The proposed tool is equipped with original and novel algorithms that transform our logics of multi-valued commitment (mv-CTLC) and multi-value trust (mv-TCTL) that we recently introduced to their classical two-valued commitment (CTLC) and trust (TCTL) logic versions as well as to Computational Tree Logic (CTL). Moreover, the tool transforms the mv-CTL to CTL, and it is applicable for the classical model checking by transforming the classical logics of trust and commitment to CTL. To demonstrate the practicality and applicability of the proposed tool in real settings, we present and report experimental results over two blockchain-based applications in the healthcare domain. Finally, we provide discussions and comparisons between the proposed approaches regarding scalability and efficiency. Moreover, we provide packages of more than 11 experiments, including the ones we conduct in this paper and enhanced experiments from previous works. Our findings ensure that the proposed approaches and the software tool that implements them are highly efficient and scalable, giving accurate results under varying conditions. • Design and implementation of an open-source scalable tool for systems model checking. • System modeling with multi-valued logic capturing uncertainty and inconsistency. • Practical demonstration using blockchain-based applications in healthcare domain. • Extensive experiments showing scalability, efficiency and reliability of the tool. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results. [ABSTRACT FROM AUTHOR]

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems. [ABSTRACT FROM AUTHOR]

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips. In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles. While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency. This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems. [ABSTRACT FROM AUTHOR]

The dried stigmas of Crocus sativus , known as "Red Gold," are highly prized for their nutritional and medicinal qualities. These qualities are due to the presence of apocarotenoids such as crocin, crocetin, safranal, and picrocrocin. The concentrations of biomolecules in saffron play a crucial role in determining its quality and commercial value. Ensuring the purity of saffron biomolecules is crucial for their pharmaceutical and food applications, given their significant potential in these fields. Enhancing cultivation practices and harnessing biosynthesis pathways through genetic engineering are widely used methods to boost the production of saffron biomolecules. Nevertheless, achieving optimal purity and output from the final products necessitates the implementation of efficient extraction methods. Additionally, employing effective identification methods is crucial for attaining accuracy in estimating the quality and quantity of these biomolecules. Hence, understanding the biosynthesis, extraction, and identification methods of saffron biomolecules, along with their significant role in human health and food, holds great importance. There is a need for extensive research to enhance the quality of components used in the food and pharmaceutical industries, surpassing the current studies. Thus, this review provides an overview of saffron biomolecules, including crocin, crocetin, safranal, and picrocrocin, along with their properties, biosynthesis, and regulation. Furthermore, the paper delves into the various aspects of saffron biomolecules, including sample preparation, extraction, identification, and quantification. In addition, the paper highlighted the possible health advantages and food uses of saffron biomolecules, as well as detailed information on the role of artificial intelligence have been discussed as a cutting-edge tool for studying various aspects of saffron biomolecules. Furthermore, the study has emphasized the future potential and opportunities for further research on saffron and its biomolecules, which can pique the interest of researchers, scientists, and industrial people seeking new and innovative opportunities. • Nutraceutical and pharmaceutical applications of saffron have been emphasized. • Chemistry and properties of crocin, picrocrocin and safranal are focussed. • Major extraction and quantification techniques of BACs from saffron are discussed. • Important sensing systems to sense aromatic compounds of saffron are highlighted. • Biosynthesis and the regulatory mechanism of BACs of saffron should be focused. [ABSTRACT FROM AUTHOR]

• Gallbladder cancer is usually detected late, so early diagnosis through ultrasound is vital. • Related studies have increased in recent years, with a focus on machine and deep learning. • Artificial intelligence enhances results and can be used as a secondary diagnostic tool. • Explainable real-time models using diverse datasets are needed to improve medical services. The occurrence of cancer in the gallbladder is infrequent. However, it is an aggressive disease that is often diagnosed at a late stage. Ultrasound imaging is a common first-line diagnostic tool for gallbladder diseases, but its accuracy relies on the expertise of the sonographer and the radiologist. The use of AI techniques, such as machine learning and deep learning, can enhance the diagnostic accuracy and efficiency from ultrasound imaging, serving as a supplementary diagnostic tool. This paper aims to address the existing gap in reviews on the application of AI in diagnosing gallbladder malignancies using ultrasound images. It provides insights into current trends in this field and suggests directions for future research. From the reviewed studies, it appears that despite the promising results, several challenges persist. These include the lack of large and comprehensive datasets, scarcity of publicly available datasets, and questions regarding the robustness, generality and reliability of AI models, which affect the models' practicality. In addition, the YOLOv8 model is evaluated as the object detector in the methodology pipeline of one of the reviewed papers. A fusion method that combines the bounding boxes of Faster R-CNN and YOLOv8, leveraging the benefits of both techniques, is also presented. By using the bounding boxes from the proposed fusion method, superior classification performance was obtained with an accuracy of 92.62%. This outperformed the individual use of Faster R-CNN and YOLOv8, which yielded accuracies of 90.16% and 82.79%, respectively. [ABSTRACT FROM AUTHOR]

This paper utilizes the skill-biased technological change and routine-biased technological change hypotheses, along with the theory of firms' innovation behavior, to establish a comprehensive analytical framework. This framework aims to elucidate the mechanisms through which technological innovation, industrial evolution, and education affect the labor skill structure in China manufacturing. The empirical results that have been gathered can be succinctly described in the following manner: (1) Research and development innovation stimulates the demand for high-and low-skilled labor, and, the rising of total factor productivity reduces the demand for low-skilled labor, confirming the labor-friendly effect of product innovation and the labor-saving effect of process innovation. (2) The cyclical pattern of fluctuating demand for low-skilled labor can be attributed to the industrial concentration, driven by technological advancements. Nevertheless, the phenomenon of cyclicality has not been observed for high-skilled labor. (3) There exists a paradoxical relationship between higher education and industrial demand, wherein education and skill are mismatched. (4) There exists the SBTC in China manufacturing resulted from process innovation. However, the SBTC does not explain the cause or mechanism behind the relative productivity shift and the higher demand for educated workers. This paper examines several policy implications including R&D innovation, employment, and education. • SBTC/RBTC and firms' innovation behavior theories are incorprated in a unified framework. • R&D inputs promote the demands for high- and low-skilled labor, and rising TFP reduces the demand for low-skilled labor. • Industrial concentration leads to a cyclical pattern of the demand for low-skilled labor rather than for high-skilled labor. • There exists a paradoxical relationship between higher education and skill demand. [ABSTRACT FROM AUTHOR]

• The emerging dependency on Electric vehicles and the role of Machine Learning in bolstering it. • Hybrid techniques tend to outwit different approaches. • Comprehensive review of various battery life estimation techniques about Machine Learning and Deep Learning. • Challenges faced and future scope of Deep Learning and Machine Learning in the State of Charge, knee-point, and Remaining Useful Life prediction of Lithium-ion batteries. Rapid adoption in the usage of li-ion battery-fueled vehicles provides a promising approach to assuage the impact of climate change. The implementation and increasing sales trends in the Electric vehicle industry have created positive results in various regions. The reliability and consistency of the Battery management system in new-age Electric vehicles to predict the remaining battery cycles, State of charge, and knee point prove to be a complex task citing the non-linear behavior of lithium-ion batteries throughout their complete lifecycle. However, to make sure that the batteries work as intended consistently, there arises a need to monitor the battery's health and performance regularly. By examining recent literature, this paper carries out a comparative study of various published research regarding estimating the State of charge for Lithium-ion batteries using multiple methods, classified into three categories: adaptive, data-driven, and hybrid approaches. We also conducted a comparative study on Knee-point and the number of charge-discharge cycles left about electric vehicles. This review intends to furnish a comparative analysis of Machine learning & Artificial intelligence-related estimation techniques and analyze their superiority over traditional data-driven methods. It was observed that hybrid techniques consisting of various Machine learning algorithms yielded good results. This paper aims to perceive the most accurate algorithms and methodologies used, which can be further used in battery management systems to improve battery prognostics drastically. The future works and challenges encountered in various researches have also been mentioned at the end of the review that will hopefully pave the way for increasing efforts towards the development of the advanced SOC, knee point, and RUL methods for future EV uses. [ABSTRACT FROM AUTHOR]