Search Results (292)

The pervasive use of screens, averaging nearly 7 h per day globally between mobile phones, computers, notebooks and TVs, has sparked a growing desire to minimize reflections from ambient lighting and enhance readability in harsh lighting conditions, without the need to increase screen brightness. This demand highlights a significant need for advanced anti-glare (AG) technologies, to increase comfort and eventually reduce energy consumption of the devices. Currently used production technologies are limited in their texture designs, which can lead to suboptimal performance of the anti-glare texture. To overcome this design limitation and improve the performance of the anti-glare feature, this work reports a new, cost-effective, high-volume production method that enables much needed design freedom over a large area. This is achieved by combining mastering via large-area Laser Beam Lithography (LBL) and replication by Nanoimprint Lithography (NIL) processes. The environmental impact of the production method, such as regards material consumption, are considered, and the full cycle from design to final imprint is discussed. Full article

Conventional methods for tumor diagnosis suffer from two inherent limitations: they are time-consuming and subjective. Computer-aided diagnosis (CAD) is an important approach for addressing these limitations. Pathology whole-slide images (WSIs) are high-resolution tissue images that have made significant contributions to cancer diagnosis and prognosis assessment. Due to the complexity of WSIs and the availability of only slide-level labels, multiple instance learning (MIL) has become the primary framework for WSI classification. However, most MIL methods fail to capture the interdependence among image patches within a WSI, which is crucial for accurate classification prediction. Moreover, due to the weak supervision of slide-level labels, overfitting may occur during the training process. To address these issues, this paper proposes a dual-attention-based multiple instance learning framework (DAMIL). DAMIL leverages the spatial relationships and channel information between WSI patches for classification prediction, without detailed pixel-level tumor annotations. The output of the model preserves the semantic variations in the latent space, enhances semantic disturbance invariance, and provides reliable class identification for the final slide-level representation. We validate the effectiveness of DAMIL on the most commonly used public dataset, Camelyon16. The results demonstrate that DAMIL outperforms the state-of-the-art methods in terms of classification accuracy (ACC), area under the curve (AUC), and F1-Score. Our model also allows for the examination of its interpretability by visualizing the dual-attention weights. To the best of our knowledge, this is the first attempt to use a dual-attention mechanism, considering both spatial and channel information, for whole-slide image classification. Full article

Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices “smart” and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security. Full article

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem. Full article

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score. Full article

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. Full article

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach. Full article

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM₁₀ dispersion in a 1.6 × 2 km² urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution. Full article

Lithium-ion batteries are pervasive in contemporary life, providing power for a vast array of devices, including smartphones and electric vehicles. With the projected sale of millions of electric vehicles globally by 2022 and over a million electric vehicles in Europe alone in the first quarter of 2023, the necessity of securing a sustainable supply of lithium-ion batteries has reached a critical point. As the demand for electric vehicles and renewable energy storage (ESS) systems increases, so too does the necessity to address the shortage of lithium batteries and implement effective recycling and recovery practices. A considerable number of electric vehicle batteries will reach the end of their useful life in the near future, resulting in a significant increase in the number of used batteries. It is of paramount importance to accurately identify the manufacturer and model of cylindrical batteries to ascertain their State of Health (SoH) and guarantee their efficient reuse. This study focuses on the automation of the identification of cylindrical cells through optical character recognition (OCR) and the analysis of the external color of the cell and the anode morphology based on computer vision techniques. This is a novel work in the current limited literature, which aims to bridge the gap between industrialized lithium-ion cell recovery processes and an automated SoH calculation. Accurate battery identification optimizes battery reuse, reduces manufacturing costs and mitigates environmental impact. The results of the work are promising, achieving 90% accuracy in the identification of 18,650 cylindrical cells. Full article

Microplastics (MPs), typically defined as plastic fragments smaller than 5 mm, are pervasive in terrestrial and marine ecosystems. There is a need for rapid, portable, low-cost detection systems to assess health and environmental risks. Fluorescent tagging with Nile Red (NR) has emerged as a popular detection method, but variations in fluorescent emissions based on NR solvent, plastic polymer, excitation wavelength, and additives complicate standardization. In this study, seven plastic samples stained with acetone-based NR were analyzed using a fluorescent spectrometer to identify optimal emission peaks across UV-Vis excitation wavelengths. These findings aid in selecting appropriate excitation wavelengths and optical filters for future detection systems. Additionally, a straightforward polymer identification scheme was validated against field-collected plastic samples, whose material composition was confirmed via Fourier Transform Infrared Spectroscopy. This work contributes towards developing accessible microplastic detection technologies by characterizing the fluorescent properties of NR-stained plastics and enhancing the capability for effective environmental monitoring. Future research will expand the dataset to include diverse plastics with varying additives and weathering, and incorporate computer-vision tools for automated data processing and polymer identification. Full article

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers. Full article

Electric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and others in parallel within custom architectures. They need to be controlled against over current, temperature, inner pressure and voltage, and their charge/discharge needs to be continuously monitored and balanced among the cells. Such a battery management system exhibits embarrassingly parallel computing, as hundreds of cells offer the opportunity for scalable and decentralized monitoring and control. In recent years, tiny machine learning has emerged as a data-driven black-box approach to address application problems at the edge by using very limited energy, computational and storage resources to achieve under mW power consumption. Examples of tiny devices at the edge include microcontrollers capable of 10–100 s MHz with 100 s KiB to few MB embedded memory. This study addressed battery management systems with a particular focus on state-of-charge prediction. Several machine learning workloads were studied by using IEEE open-source datasets to profile their accuracy. Moreover, their deployability on a range of microcontrollers was studied, and their memory footprints were reported in a very detailed manner. Finally, computational requirements were proposed with respect to the parallel nature of the battery system architecture, suggesting a per cell and per module tiny, decentralized artificial intelligence system architecture. Full article

The pervasiveness of technologies leads us to talk about a code society. From an educational point of view, coding, computational thinking, and educational robotics are an open possibility. Nevertheless, new elements such as artificial intelligence are rapidly changing educational technology perspectives. In this work, we will analyze school policies and theoretical bases in order to understand if, and under what kind of, condition coding, computational thinking, and educational robotics still represent the qualifying elements of a framework for digital literacy and digital citizenship. Full article

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human–machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction. Full article

Academic interest in AI in journalism has been growing since 2018. Through a systematic review of the literature from 2014 to 2023, this study discusses the evolution of research in the field and how AI has changed journalism. The aim is to understand the impact of AI on journalism, based on a review of academic papers and a qualitative analysis of the most cited articles. This study combines: a systematic review of scientific articles extracted from Web of Science and Scopus (n = 699) and a qualitative approach with categorical content analysis of those with more than 50 citations (n = 59). The results (n = 699) highlight the prominence of authors from the Universities of Amsterdam and Santiago de Compostela. The United States has the largest number of authorships: 261 distributed across 99 institutions. The categorical content analysis (n = 59) shows a focus on issues like the work of the journalist, because AI is replacing journalists with repetitive and monotonous tasks, raising several questions about the role of the journalist. The findings show the rise of computational methods, highlighting the pervasiveness of AI in research, which has not been explored in previous work. Ethics, regulation, and journalism education remain under-discussed in research. Full article