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This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research. [ABSTRACT FROM AUTHOR]

Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities. [ABSTRACT FROM AUTHOR]

Gravitational wave research presents a range of intriguing challenges, each of which has driven significant progress in the field. Key research problems include glitch classification, glitch cancellation, gravitational wave denoising, binary black hole signal detection, gravitational wave bursts, and minor issues that contribute to the overall understanding of gravitational wave phenomena. This paper explores the applications of artificial intelligence, deep learning, and machine learning techniques in addressing these challenges. The main goal of the paper is to provide an effective view of AI and deep learning usage for gravitational wave analysis. Thanks to the advancements in artificial intelligence and machine learning techniques, aided by GPUs and specialized software frameworks, these techniques have played a key role over the last decade in the identification, classification, and cancellation of gravitational wave signals, as presented in our results. This paper provides a comprehensive exploration of the adoption rate of these techniques, with reference to the software and hardware involved, their effectiveness, and potential limitations, offering insights into the advancements in the analysis of gravitational wave data. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth exploration of the transformative impact of integrating the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) within the domain of aviation maintenance. It articulates the transition from conventional health monitoring practices to a more advanced, comprehensive health management approach, leveraging these modern technologies. This paper emphasizes the pivotal shift from reactive maintenance strategies to proactive and predictive maintenance paradigms, facilitated by the real-time data collection capabilities of IoT devices and the analytical prowess of AI. This transition not only enhances the safety and reliability of flight operations but also optimizes maintenance procedures, thereby reducing operational costs and improving efficiency. This paper meticulously outlines the implementation challenges, including technological integration, regulatory compliance, and security concerns, while proposing a future research agenda to address these issues and further harness the potential of these technologies in revolutionizing aviation maintenance. [ABSTRACT FROM AUTHOR]

International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The findings contribute to a better understanding of the potential impact of AI on international students' educational experiences and offer insights into the integration of AI into educational administration and learning processes. [ABSTRACT FROM AUTHOR]

This paper explores the impact of Artificial Intelligence (AI) on the role of dental assistants and nurses in orthodontic practices, as there is a gap in understanding the currently evolving impact on orthodontic treatment workflows. The introduction of AI-language models such as ChatGPT 4 is changing patient-office communication and transforming the role of orthodontic nurses. Teledentistry is now heavily reliant on AI implementation in orthodontics. This paper presents the proof of a novel concept: an AI-powered orthodontic workflow that provides new responsibilities for an orthodontic nurse. It also provides a report of an assessment of such a workflow in an orthodontic practice that uses an AI solution called Dental Monitoring over a period of three years. The paper evaluates the benefits and drawbacks of daily automated assessments of orthodontic treatment progress, the impact of AI on personalized care, and the new role of a dental assistant. The paper concludes that AI will improve dental practice through more precise and personalized treatment, bringing new roles and responsibilities for trained medical professionals but raising new ethical and legal issues for dental practices. [ABSTRACT FROM AUTHOR]

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals. [ABSTRACT FROM AUTHOR]

Nowadays, one of the important aspects of research on call/contact centre (CC) systems is how to automate their operations. Process automation is influenced by the continuous development in the implementation of virtual assistants. The effectiveness of virtual assistants depends on numerous factors. One of the most important is correctly recognizing the intent of clients conversing with the machine. Recognizing intentions is not an easy process, as often the client's actual intentions can only be correctly identified after considering the client's emotional state. When it comes to human–machine communication, the ability of a virtual assistant to recognize the client's emotional state would greatly improve its effectiveness. This paper proposes a new method for recognizing interlocutors' emotions dedicated directly to contact centre systems. The developed method provides opportunities to determine emotional states in text and voice channels. It provides opportunities to explore both the client's and the agent's emotional states. Information about agents' emotions can be used to build their behavioural profiles, which is also applicable in contact centres. In addition, the paper explored the possibility of emotion assessment based on automatic transcriptions of recordings, which also positively affected emotion recognition performance in the voice channel. The research used actual conversations that took place during the operation of a large, commercial contact centre. The proposed solution makes it possible to recognize the emotions of customers contacting the hotline and agents handling these calls. Using this information in practical applications can increase the efficiency of agents' work, efficiency of bots used in CC and increase customer satisfaction. [ABSTRACT FROM AUTHOR]

Industry 4.0, which was proposed ten years ago to address both the industry's strengths and faults, has finally been replaced by Industry 5.0. It seeks to put human welfare at the core of manufacturing systems, achieving societal goals beyond employment and growth to firmly provide wealth for the long-term advancement of all of humanity. The purpose of this research is to examine the risks involved in the adoption of Industry 5.0's architecture. The paper discusses the significance of Industry 5.0 and the advanced technology needed for this industrial revolution, followed by a detailed discussion of Industry 5.0's human-centric strategy. The comprehensive literature review has resulted in the identification of risks and their mitigation strategies in Industry 5.0 architecture. A taxonomy with respect to different categories of risks has also been proposed. This study classifies Industry 5.0 system assets, identifies platform-independent risks, and develops countermeasures to protect against potential threats, irrespective of the business or domain. [ABSTRACT FROM AUTHOR]

Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies. [ABSTRACT FROM AUTHOR]

Urban tree cadastres, crucial for climate adaptation and urban planning, face challenges in maintaining accuracy and completeness. A transdisciplinary approach in Kaiserslautern, Germany, complements existing incomplete tree data with additional precise GPS locations of urban trees. Deep learning models using aerial imagery identify trees, while other applications employ street view imagery and LIDAR data to collect additional attributes, such as height and crown width. A web application encourages citizen participation in adding features like species and improving datasets for further model training. The initiative aims to minimize resource-intensive maintenance conducted by local administrations, integrate additional features, and improve data quality. Its primary goal is to create transferable AI models utilizing aerial imagery and LIDAR data that can be applied in regions with similar tree populations. The approach includes tree clusters and private trees, which are essential for assessing allergy and ozone potential but are usually not recorded in municipal tree cadastres. The paper highlights the potential of improving tree cadastres for effective urban planning in a transdisciplinary approach, taking into account climate change, health, and public engagement. [ABSTRACT FROM AUTHOR]

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems. [ABSTRACT FROM AUTHOR]

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world's water supply concerns effectively. [ABSTRACT FROM AUTHOR]

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there is currently no effective method for determining a single ground truth value or landmark from a set of annotations in regression problems. We propose a novel method for deriving ground truth labels in regression problems that considers the performance and precision of individual annotators when identifying each label separately. In contrast to the commonly accepted method of computing the global mean, our method does not assume each annotator to be equally capable of completing the specified task, but rather ensures that higher-performing annotators have a greater contribution to the final result. The ground truth selection method described within this paper provides a means of improving the quality of input data for machine learning model development by removing lower-quality labels. In this study, we objectively demonstrate the improved performance by applying the method to a simulated dataset where a canonical ground truth position can be known, as well as to a sample of data collected from crowd-sourced labels. [ABSTRACT FROM AUTHOR]

The field of signal processing using machine and deep learning algorithms has undergone significant growth in the last few years, with a wide scope of practical applications for electroencephalography (EEG). Transcutaneous electroacupuncture stimulation (TEAS) is a well-established variant of the traditional method of acupuncture that is also receiving increasing research attention. This paper presents the results of using deep learning algorithms on EEG data to investigate the effects on the brain of different frequencies of TEAS when applied to the hands in 66 participants, before, during and immediately after 20 min of stimulation. Wavelet packet decomposition (WPD) and a hybrid Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model were used to examine the central effects of this peripheral stimulation. The classification results were analysed using confusion matrices, with kappa as a metric. Contrary to expectation, the greatest differences in EEG from baseline occurred during TEAS at 80 pulses per second (pps) or in the 'sham' (160 pps, zero amplitude), while the smallest differences occurred during 2.5 or 10 pps stimulation (mean kappa 0.414). The mean and CV for kappa were considerably higher for the CNN-LSTM than for the Multilayer Perceptron Neural Network (MLP-NN) model. As far as we are aware, from the published literature, no prior artificial intelligence (AI) research appears to have been conducted into the effects on EEG of different frequencies of electroacupuncture-type stimulation (whether EA or TEAS). This ground-breaking study thus offers a significant contribution to the literature. However, as with all (unsupervised) DL methods, a particular challenge is that the results are not easy to interpret, due to the complexity of the algorithms and the lack of a clear understanding of the underlying mechanisms. There is therefore scope for further research that explores the effects of the frequency of TEAS on EEG using AI methods, with the most obvious place to start being a hybrid CNN-LSTM model. This would allow for better extraction of information to understand the central effects of peripheral stimulation. [ABSTRACT FROM AUTHOR]

Featured Application: This work is beneficial to bridge the gap between advanced technologies and their applications in the manufacturing industry, especially for SMEs. The fourth industrial revolution is the transformation of industrial manufacturing into smart manufacturing. The advancement of digital technologies that make the trend Industry 4.0 are considered as the transforming force that will enable this transformation. However, Industry 4.0 digital technologies need to be connected, integrated and used effectively to create value and to provide insightful information for data driven manufacturing. Smart manufacturing is a journey and requires a roadmap to guide manufacturing organizations for its adoption. The objective of this paper is to review different methodologies and strategies for smart manufacturing implementation to propose a simple and a holistic roadmap that will support the transition into smart factories and achieve resilience, flexibility and sustainability. A comprehensive review of academic and industrial literature was preformed based on multiple stage approach and chosen criteria to establish existing knowledge in the field and to evaluate latest trends and ideas of Industry 4.0 and smart manufacturing technologies, techniques and applications in the manufacturing industry. These criteria are sub-grouped to fit within various stages of the proposed roadmap and attempts to bridge the gap between academia and industry and contributes to a new knowledge in the literature. This paper presents a conceptual approach based on six stages. In each stage, key enabling technologies and strategies are introduced, the common challenges, implementation tips and case studies of industrial applications are discussed to potentially assist in a successful adoption. The significance of the proposed roadmap serve as a strategic practical tool for rapid adoption of Industry 4.0 technologies for smart manufacturing and to bridge the gap between the advanced technologies and their application in manufacturing industry, especially for SMEs. [ABSTRACT FROM AUTHOR]

Recently, many farmers have started using robots to help with labour-intensive harvesting operations and deal with labour shortage that was also a negative consequence of the recent COVID-19 pandemic. Intelligent harvesting robots make farming more efficient and productive. However, and like any other technology, intelligent harvesting robots come with a security risk, as threats can damage the robotic system and wreak havoc before the farmer/operator realizes it. This paper focuses on analysing the threats against the security of harvesting robots alongside with the safety implications that may rise if the robotic system is compromised. We analysed an actual asparagus harvesting robot and looked at others in the literature. We identified several security threats which we classified into five categories: network, hardware, software, Artificial Intelligence (AI) and cloud security issues. We selected three interesting attack scenarios for a deeper analysis. Our results suggest that these robots have a large attack surface that can lead to exploits with immense financial and operational impacts. [ABSTRACT FROM AUTHOR]

Urban water supply systems are complex and dynamic in nature, and as a result, can be considered complex to manage owing to enhanced urbanization levels, climate change, growing and varying consumer demands, and limited water resources. The operation of such a system must be managed effectively for sustainable water supply to satisfy the growing consumer demand. This creates a need for intelligent systems for the purposes of operational management. In recent years, computing technologies have been applied to water systems to assist water utilities in addressing some of these problems. Unfortunately, previous ICT solutions have not been able to provide the necessary support for applications to deal with the dynamics of water supply system environments. With the increasing growth in technology, the water sector is moving to the full phase of digitalization to enhance the sustainability of systems. Thus, a new industrial revolution in the water context (Water 4.0) is being researched. This is referred to as Water 4.0, which offers better possibilities to enhance the sustainability of water supply system operations. This paper presents an overview of Water 4.0 and its applications in enhancing water supply system operations. Key features of Water 4.0 are discussed. Furthermore, challenges and future opportunities offered by technology for sustainable operation of municipal water services are discussed. [ABSTRACT FROM AUTHOR]

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

The theory and applications of Smart Factories and Industry 4.0 are increasing the entry into the industry. It is common in industry to start converting exclusive parts, of their production, into this new paradigm rather than converting whole production lines all at once. In Europe and Sweden, recent political decisions are taken to reach the target of greenhouse gas emission reduction. One possible solution is to replace concrete in buildings with Cross Laminated Timber. In the last years, equipment and software that have been custom made for a certain task, are now cheaper and can be adapted to fit more processes than earlier possible. This in combination, with lessons learned from the automotive industry, makes it possible to take the necessary steps and start redesigning and building tomorrows automated and flexible production systems in the wood industry. This paper presents a proof of concept of an automated inspection system, for wood surfaces, where concepts found in Industry 4.0, such as industrial Internet of things (IIoT), smart factory, flexible automation, artificial intelligence (AI), and cyber physical systems, are utilized. The inspection system encompasses, among other things, of the shelf software and hardware, open source software, and standardized, modular, and mobile process modules. The design of the system is conducted with future expansion in mind, where new parts and functions can be added as well as removed. [ABSTRACT FROM AUTHOR]

In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A2G algorithm, integrating the finite element method among concerns over data security. Our A2G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards. [ABSTRACT FROM AUTHOR]

Featured Application: A Smart Capsule enabling medical staff to activate intuitively autonomous delivery mission of drones for transportation of medical materials. In the last few years, many examples of blood and medicine delivery drones have been demonstrated worldwide, which mainly rely on aeronautical experience that is not common in the medical world. Speaking about drone delivery, attention should focus on the most important thing: the transported lifesaving good. Traditional boxes that monitor temperature are not usually in real time, and are not suitable for drone transportation because they are heavy and bulky. This means that the biomedical characteristics of delivery are of primary importance. A Smart Capsule, equipped with artificial intelligence (AI), is the first system ever proposed to provide a fully autonomous drone delivery service for perishable and high-value medical products, integrating real-time quality monitoring and control. It consists in a smart casing that is able to guide any autonomous aerial vehicle attached to it, specifically designed for transporting blood, organs, tissues, test samples and drugs, among others. The system monitors the conditions of the product (e.g., temperature, agitation and humidity) and adjusts them when needed by exploiting, for instance, vibrations to maintain the required agitation, ensuring that goods are ready to be used as soon as they are delivered. The Smart Capsule also leverages external temperature to reduce energy uptake from the drone, thus improving the drone's battery life and flight range. The system replaces the need for specialized drivers and traditional road-bound transportation means, while guaranteeing compliance with all applicable safety regulations. A series of 16 experimental tests was performed to demonstrate the possibility of using the smart capsule to manage the flight and internal good delivery. Eighty-one missions were carried out for a total of 364 min of flight. The Smart Capsule greatly improves emergency response and efficiency of healthcare systems by reducing delivery times by up to 80% and costs by at least 28%. The Smart Capsule and its enabling technology based on AI for drone deliveries are discussed in this paper. The aim of this work is to show the possibility of managing drone delivery with an AI-based device. [ABSTRACT FROM AUTHOR]

An observation on the current state of teaching large language models (LLMs) in education is made. The problem of lacking a structural approach is defined. A methodology is created in order to serve as the basis of a workshop in teaching students with different types of backgrounds the correct use of LLMs and their capabilities. A workshop plan is created; instructions and materials are presented. A practical experiment has been conducted by dividing students into teams and guiding them to create a small project. Different LLMs are used for the purposes of creating a fictional story, images relating to the story, and very simple HTML, JS, and CSS code. Participants are given requirements that consider the limitations of LLMs, and different approaches to creatively solving arising issues due to the requirements are observed. The students' projects are hosted on the web, so that they can see the results of their work. They are given the opportunity to use them as motivation for their future development. A survey is created and distributed to all the participating students. The results are analyzed from different angles and conclusions are made on the effectiveness of the workshop in completing its goal of solving the defined problem. [ABSTRACT FROM AUTHOR]

The increase in recent power failures, with negative impacts on humans and the economy, has been largely attributed to environmental effects and the aging of the power network. These have been accelerated in the last years due to two main factors: an increased load on the power network and material degradation owing to the presence of environmental pollutants. These factors together with specific weather conditions create the incipient conditions for power network degradation. In this paper, a review of the influence of environmental factors on high-voltage (HV) materials and components is provided. Sensing and artificial intelligence (AI) technologies developed to prevent the failure of the material structure and HV components are also reported. [ABSTRACT FROM AUTHOR]

In the evolving field of machine learning, deploying fair and transparent models remains a formidable challenge. This study builds on earlier research, demonstrating that neural architectures exhibit inherent biases by analyzing a broad spectrum of transformer-based language models from base to x-large configurations. This article investigates movie reviews for genre-based bias, which leverages the Word Embedding Association Test (WEAT), revealing that scaling models up tends to mitigate bias, with larger models showing up to a 29% reduction in prejudice. Alternatively, this study also underscores the effectiveness of prompt-based learning, a facet of prompt engineering, as a practical approach to bias mitigation, as this technique reduces genre bias in reviews by more than 37% on average. This suggests that the refinement of development practices should include the strategic use of prompts in shaping model outputs, highlighting the crucial role of ethical AI integration to weave fairness seamlessly into the core functionality of transformer models. Despite the basic nature of the prompts employed in this research, this highlights the possibility of embracing structured prompt engineering to create AI systems that are ethical, equitable, and more responsible for their actions. [ABSTRACT FROM AUTHOR]

Regenerative dentistry has experienced remarkable advancement in recent years. The interdisciplinary discoveries in stem cell applications and scaffold design and fabrication, including novel techniques and biomaterials, have demonstrated immense potential in the field of tissue engineering and regenerative therapy. Scaffolds play a pivotal role in regenerative dentistry by facilitating tissue regeneration and restoring damaged or missing dental structures. These biocompatible and biomimetic structures serve as a temporary framework for cells to adhere, proliferate, and differentiate into functional tissues. This review provides a concise overview of the evolution of scaffold strategies in regenerative dentistry, along with a novel analysis (Bard v2.0 based on the Gemini neural network architecture) of the most commonly employed materials used for scaffold fabrication during the last 10 years. Additionally, it delves into bioprinting, stem cell colonization techniques and procedures, and outlines the prospects of regenerating a whole tooth in the future. Moreover, it discusses the optimal conditions for maximizing mesenchymal stem cell utilization and optimizing scaffold design and personalization through precise 3D bioprinting. This review highlights the recent advancements in scaffold development, particularly with the advent of 3D bioprinting technologies, and is based on a comprehensive literature search of the most influential recent publications in this field. [ABSTRACT FROM AUTHOR]

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users' preferences and collective behavior. Results: A so-called virtual village "Cap de Ballon" was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person's apartment. Conclusions: The overall concept of the VR village's utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity. [ABSTRACT FROM AUTHOR]

In this study, we applied an optimal deep learning algorithm to detect defects in OLED cells. This study aims to enhance the yield of OLEDs by reducing the number of defective products through defect detection in OLED cells. Defects in OLED cells can arise owing to various factors, but dark spots are predominantly identified and studied. Therefore, actual dark spot images were required for this study. However, obtaining real data is challenging because of security concerns in the OLED industry. Therefore, a Solver program utilizing the finite element method (FEM) was employed to generate 2000 virtual dark spot images. The generated images were categorized into two groups: initial images of dark spots and images obtained after 10,000 h. The pixel values of these dark spot images were adjusted for efficient recognition and analysis. Furthermore, CNN, ResNet-50, and VGG-16 were implemented to apply the optimal deep learning algorithms. The results showed that the VGG-16 algorithm performed the best. A defect detection model was created based on the performance metrics of the deep learning algorithms. The model was trained using 1300 dark spot images and validated using 600 dark spot images. The validation results indicated an accuracy of 0.988 and a loss value of 0.026. A defect detection model that utilizes the VGG-16 algorithm was considered suitable for distinguishing dark spot images. To test the defect detection model, 100 images of dark spots were used. The experimental results indicated an accuracy of 89%. The images were classified as acceptable or defective based on the threshold values. By applying the VGG-16 deep learning algorithm to the defect detection model, we can enhance the yield of OLED products, reduce production costs, and contribute significantly to the advancement of OLED display manufacturing technology. [ABSTRACT FROM AUTHOR]

Parkinson's Disease and Adductor-type Spasmodic Dysphonia are two neurological disorders that greatly decrease the quality of life of millions of patients worldwide. Despite this great diffusion, the related diagnoses are often performed empirically, while it could be relevant to count on objective measurable biomarkers, among which researchers have been considering features related to voice impairment that can be useful indicators but that can sometimes lead to confusion. Therefore, here, our purpose was aimed at developing a robust Machine Learning approach for multi-class classification based on 6373 voice features extracted from a convenient voice dataset made of the sustained vowel/e/ and an ad hoc selected Italian sentence, performed by 111 healthy subjects, 51 Parkinson's disease patients, and 60 dysphonic patients. Correlation, Information Gain, Gain Ratio, and Genetic Algorithm-based methodologies were compared for feature selection, to build subsets analyzed by means of Naïve Bayes, Random Forest, and Multi-Layer Perceptron classifiers, trained with a 10-fold cross-validation. As a result, spectral, cepstral, prosodic, and voicing-related features were assessed as the most relevant, the Genetic Algorithm performed as the most effective feature selector, while the adopted classifiers performed similarly. In particular, a Genetic Algorithm + Naïve Bayes approach brought one of the highest accuracies in multi-class voice analysis, being 95.70% for a sustained vowel and 99.46% for a sentence. [ABSTRACT FROM AUTHOR]

This study aimed to enhance the stability and security of power plant control network systems by developing detectable models using artificial intelligence machine learning techniques. Due to the closed system operation policy of facility manufacturers, it is challenging to detect and respond to security threats using standard security systems. With the increasing digitization of control systems, the risk of external malware penetration is also on the rise. To address this, machine learning techniques were applied to extract patterns from network traffic data produced at an average of 6.5 TB per month, and fingerprinting was used to detect unregistered terminals accessing the control network. By setting a threshold between transmission amounts and attempts using one month of data, an anomaly judgment model was learned to define patterns of data communication between the origin and destination. The hypothesis was tested using machine learning techniques if a new pattern occurred and no traffic occurred. The study confirmed that this method can be applied to not only plant control systems but also closed-structured control networks, where availability is critical, and other industries that use large amounts of traffic data. Experimental results showed that the proposed model outperformed existing models in terms of detection efficiency and processing time. [ABSTRACT FROM AUTHOR]

The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the "smart cities" of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city's infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed. [ABSTRACT FROM AUTHOR]

It is undeniable that the adoption of blockchain- and artificial intelligence (AI)-based paradigms is proceeding at lightning speed. Both paradigms provide something new to the market, but the degree of novelty and complexity of each is different. In the age of digital money, blockchains may automate installments to allow for the safe, decentralized exchange of personal data, information, and logs. AI and blockchains are two of the most talked about technologies right now. Using a decentralized, secure, and trustworthy system, blockchain technology can automate bitcoin payments and provide users access to a shared ledger of records, transactions, and data. Through the use of smart contracts, a blockchain may also regulate user interactions without the need for a central authority. As an alternative, AI provides robots with the ability to reason and make decisions and human-level intellect. This revelation led to a thorough assessment of the AI and blockchain combo created between 2012 and 2022. This critical review contains 121 articles from the recent decade that investigate the present situation and rationale of the AI and blockchain combination. The integration's practical application is the emphasis of this overview's meatiest portion. In addition, the gaps and problems of this combination in the linked literature have been studied, with a focus on the constraints. [ABSTRACT FROM AUTHOR]

The IoT's quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification. [ABSTRACT FROM AUTHOR]

Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework. [ABSTRACT FROM AUTHOR]

Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students' success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented. [ABSTRACT FROM AUTHOR]

Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%. [ABSTRACT FROM AUTHOR]

Video display content can be extended to the walls of the living room around the TV using projection. The problem of providing appropriate projection content is hard for the computer and we solve this problem with deep neural network. We propose the peripheral vision system that provides the immersive visual experiences to the user by extending the video content using deep learning and projecting that content around the TV screen. The user may manually create the appropriate content for the existing TV screen, but it is too expensive to create it. The PCE (Pixel context encoder) network considers the center of the video frame as input and the outside area as output to extend the content using supervised learning. The proposed system is expected to pave a new road to the home appliance industry, transforming the living room into the new immersive experience platform. [ABSTRACT FROM AUTHOR]

Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has a limitation in that it is only in the passive cognition area, so its operating process is not transparent; therefore, the technology relies on learning data. Since raw data for AI learning are processed and inspected manually to assure high quality for sophisticated AI learning, human errors are inevitable, and damaged and incomplete data and differences from the original data may lead to unexpected outputs of AI learning for which processed data are used. In this context, this research examines cases where AI learning data were inaccurate, in terms of cybersecurity, and the need for learning data management before machine learning through analysis of cybersecurity attack techniques, and we propose the direction of establishing a data-preserving AI system, which is a blockchain-based learning data environment model to verify the integrity of learning data. The data-preserving AI learning environment model is expected to prevent cyberattacks and data deterioration that may occur when data are provided and utilized in an open network for the processing and collection of raw data. [ABSTRACT FROM AUTHOR]