A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Purpose: The article presents the trajectory of change in ergonomic research, beginning with the pioneering 1857 publication by Wojciech Bogumił Jastrzębowski and continuing to the present day (2025). The aim is to trace the development of ergonomic theory and practice in the context of advanced information technologies characteristic of Industry 4.0 and 5.0. The study seeks to highlight how integrating ergonomics into modern industry has transformed the discipline and what future ergonomic approaches must entail. Design/methodology/approach: The objectives of the paper were achieved through a conceptual and historical analysis combined with elements of critical literature review and foresight. Rather than conducting empirical research, the authors trace the evolution of ergonomics from its origins in the 19th century to contemporary challenges associated with Industry 5.0. The approach is interdisciplinary, drawing from ergonomics, systems engineering, neurocognitive science, and social philosophy. The paper critically examines transformations in the human–technology relationship, forecasting the future role of ergonomics in increasingly automated and AI-driven environments. Findings: The analysis reveals that the nature of ergonomics must fundamentally change in response to new conditions of human interaction with intelligent technical systems using artificial intelligence. The study shows that contemporary ergonomics increasingly extends beyond traditional applications, evolving toward human-centered organizational design. Moreover, the integration of ergonomic thinking into the design of objects and workplaces enables the discipline to synergize with systems engineering, enhancing its strategic role within enterprises. Originality/value: This paper offers a unique historical-to-futuristic perspective on ergonomics, identifying its shift from a human-as-operator paradigm toward human-as-central agent within technological systems. The value of the article lies in its relevance to researchers, system engineers, and enterprise decision-makers seeking to understand and implement ergonomic policies in the era of smart industry and human-centric automation. Keywords: Human-centered design; Ergonomics; Industry 5.0; Ergonomic policy; Human machine interaction. Category of the paper: Viewpoint.

Incorporating biological molecular interactions into cognitive computing through chemical artificial intelligence (AI) presents a transformative approach with far-reaching implications for various fields, such as protein engineering, drug discovery, bioinformatics, synthetic biology, and unconventional computing. Cognitive computing, designed to emulate human thought processes and enhance decision-making, utilizes technologies, such as machine learning, natural language processing, and speech recognition for better human-system interactions. Despite advancements, the integration of biological processes with cognitive computing remains fraught with challenges, particularly due to the complexity and scale of biological data. Here, we explore the possible benefits of connecting cognitive computing with biological knowledge, including more precise models of protein interactions, gene regulation, and metabolic pathways, which could lead to personalized treatments and early disease detection. Furthermore, we discuss the intersection of cognitive computing and biophysical research techniques, examining how analogies from neuroscience-like synaptic communication and neural plasticity-can inform the development of neuromorphic chips and enhance predictive models. Additionally, the study delves into intrinsically disordered proteins (IDPs) and their crucial roles in brain function and information processing. These insights are pivotal for advancing neuroinformatics and creating more adaptive, context-aware cognitive computing algorithms. By leveraging biophysical investigations and the unique properties of IDPs, the research aims to bridge the gap between the biological processes and their computational analogs, proposing novel methods, such as chemical AI implemented in liquid solutions as promising avenues for future advancements.

A inovação tecnológica se mantém como relevante fator de crescimento econômico no contexto capitalista dinâmico, marcado pelo avanço das Tecnologias de Informação e Comunicação (TIC), no qual as organizações articulam capacidades dinâmicas na busca de vantagem competitiva sustentável, principalmente através de competências individuais. Nesse cenário, destacam-se a Indústria 4.0 (I4.0) e a Transformação Digital (TD), que inclui Big Data, Internet of Things (IoT) e Artificial Intelligence (AI), com ferramentas baseadas em algoritmos complexos, machine learning e deep learning. Podem servir à mineração de dados, análises preditivas e interação com pessoas através de Agentes Virtuais Inteligentes (AVI) que utilizam linguagem natural e aprendizado contínuo, formas de emular habilidades humanas complexas. Deste modo, os elementos da TD se apresentam como novas tecnologias que impactam a Organização do Trabalho (OT) e intensificam os desafios relacionados à integração da função estratégica da Gestão de Recursos Humanos (GRH), suas práticas e instrumentos, com exemplos de produtos (como HR analytics, people analytics e Artificial Intelligence, oferecidos por startups/spin-offs de HR Tech. Sob essas perspectivas, é proposto discutir o uso de IA (Computação Cognitiva) a partir do caso de um banco brasileiro, inicialmente através da percepção de seus funcionáros, atendidos por um AVI que presta informações sobre os produtos e serviços desta empresa, atividade normalmente atribuída aos atendentes do departamento de contact center. Como complemento, buscou-se observar se o uso dessa nova tecnologia, voltada a processos bancários, também inclui processos de RH.

Bayesian network utilizes graphical model to describe dependencies among variables in probabilistic way, it is one of the most important model for uncertainty processing in Artificial Intelligence. Incremental learning of Bayesian networks has been received more attentions in recent years, in this paper a novel method is proposed to learn Bayesian network from incremental data. In this method, a novel incremental scoring function is designed to adaptively adjust the tendency of matching new and old data in the process of incremental learning. We propose an improved adaptive incremental structure learning algorithm for Bayesian network. Theoretical analysis and experimental results both demonstrate the proposed method outperforms other state-of-the-art methods.

Achieving operational excellence and efficiency is a constant goal in manufacturing and production, driven by the need to reduce waste and optimize processes in today’s competitive environment. The era of Industry 4.0 offers advanced technologies such as Artificial Intelligence (AI), robotics, IoT and cloud computing to support this pursuit, aligning with philosophies like lean manufacturing. However, modeling and optimizing complex production systems, which involve nonlinear behavior, interdependencies and time delays, remains a significant challenge. This research proposes a hybrid AI solution for optimizing production efficiency by combining Data Envelopment Analysis (DEA), Machine Learning (ML)-based simulation and Genetic Algorithms (GA). DEA is used to identify the efficient frontier of the production system based on historical or synthetic data. This data is used to train an ML model capable of simulating system behavior. GA is then applied to search for the optimal input configurations and control parameters to maximize efficiency, considering operational constraints. The methodology was applied and validated using real sensor data from a thermoelectric power plant. The process included data preparation with outlier removal using the Z-score method, calculation of relative efficiency using DEA-CCR, training ML models to predict outputs and efficiencies, and applying GA to identify ideal settings. The implementation resulted in significant improvements, with efficiency gains of 25% and 58% in two tested models, outperforming conventional closed-loop control systems. The approach also demonstrated that separating the optimization phase using GA from the DEA and ML training phases can significantly reduce computational time. As a result, the solution not only optimizes resource use and reduces production costs but also provides greater flexibility to production systems by reducing the direct dependency between high output levels and overall efficiency. The developed framework presents a practical, accessible and effective solution for production systems. It is adaptable to various industrial sectors and contributes to the data-driven digital transformation within the scope of Industry 4.0.

Highlights AI can enhance agricultural safety through predictive models, LLMs, computer vision, and wearables. Machine learning is a tool that has the potential to predict hazards using historical data on injury incidents, weather, and worker behavior. Computer vision and remote sensing applications could be used to detect unsafe conditions for real-time risk mitigation. Wearable AI devices present opportunities to monitor worker health and prevent injuries in agricultural environments. ABSTRACT. Artificial intelligence (AI) has emerged as a transformative tool in various industries, including agriculture, where it has the potential to enhance safety and reduce injury risks. This review explores the application of AI techniques in occupational safety with a special focus on agricultural safety, focusing on predictive modeling, large language models (LLMs), computer vision, and wearable technologies. Due to the limited number of studies that address AI in agricultural safety, research from related fields, such as construction safety, was also considered for its potential applicability. The findings indicate that (1) predictive models that leverage machine learning (ML) algorithms can assess historical data and forecast hazards, enabling proactive safety measures. (2) LLMs can improve injury report analysis by extracting key terms and patterns to identify recurring risks. (3) Computer vision and remote sensing technologies enhance environmental monitoring by detecting real-time unsafe conditions. At the same time, (4) AI-powered wearable devices can track worker health indicators such as heart rate, potentially preventing injuries. A total of 85 studies were analyzed, providing insights into the diverse applications of AI in mitigating occupational hazards, specifically agricultural hazards. This review highlights the current advancements and future research opportunities for AI-driven safety interventions in high-risk occupational environments. Keywords: Agricultural safety, Artificial intelligence, Computer vision, Health, Injury prevention, Large language models, Machine learning, Occupational safety, Predictive modeling, Wearable technology.

Introduction: Unprecedented opportunities and difficulties have arisen as a result of the quick expansion of digital environments, especially in the fields of cybersecurity and decision support systems. More clever and flexible strategies are required because traditional security measures frequently can't keep up with new threats. In intelligent decision support systems (IDSS), cognitive computing—which combines artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and sophisticated data analytics—has become a disruptive force. These systems can process enormous volumes of structured and unstructured data, identify patterns, and offer proactive insights for improved decision-making in security-critical circumstances by simulating human cognitive processes. The reconfiguration of IDSS by cognitive computing to improve the security and resilience of digital ecosystems is examined in this chapter. In order to reduce cyber risks, we go over the use of AI-driven threat intelligence, real-time anomaly detection, and predictive risk assessment. Furthermore, we investigate how adaptive and self-learning algorithms in cognitive computing improve fraud detection, network security, and access control. By integrating explainable AI (XAI), security decision-making becomes transparent, resolving issues with trust and accountability in automated security systems. The chapter also explores new developments that will influence cognitive-driven IDSS in the future, such as federated learning for decentralised threat intelligence sharing, autonomous security agents, and the possible effects of quantum computing on cryptographic security. We also draw attention to the difficulties in putting cognitive security solutions into practice, including computational complexity, hostile AI model assaults, and moral dilemmas in automated decision-making. Objectives: This study paper's main goal is to examine current developments, new trends, and potential paths for digital environment security. Modern cyber risks are addressed by this study's analysis of cutting-edge technology, developing cybersecurity frameworks, and clever defence measures. Additionally, the study looks into how security methods are changing as a result of developments in blockchain, zero-trust architecture, quantum cryptography, artificial intelligence, and cybersecurity automation. In order to improve the resilience, privacy, and integrity of digital systems in a world that is becoming more linked, the study also aims to highlight current issues and suggest possible solutions. Methods: The qualitative research methodology used in this study includes a review of recent cybersecurity innovations-related literature, research articles, and case studies. Information was gathered from reliable sources, including industry publications, scholarly journals, and professional comments. In order to secure digital environments, the study compares traditional and modern security methods, analyses emerging technologies, and looks for future trends and solutions. Results: According to the survey, cutting-edge technologies like blockchain, artificial intelligence (AI), quantum cryptography, and zero-trust architecture are all significantly improving cybersecurity. Blockchain guarantees safe data exchanges, while AI aids in danger

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

tailored to support and assist individuals on the autism spectrum, helping them improve their social skills and overall well-being. Nowadays, they are crucial tools for therapy that can improve social skills in a short period.In conclusion, this editorial paper highlights the remarkable progress made in AI and neural networks, shedding light on their applications in human activity recognition, medical image steganography, lower limb prosthetics, assistive robots for ASD, and wearable assistive devices for visually impaired individuals. The discussed advancements in spiking neural P systems, geneticbased steganography algorithms, systematic analysis of prosthetic technology, energy optimization of assistive robots, and the Tiny-Yolov3 architecture demonstrate the transformative potential of AI in improving various aspects of human life. A systematic literature review of the current technologies for developing lower limb prosthetics reveals a need for a specific, generalized structure for new developments. Notable gaps exist in energy management and improve, smoother patient interaction, thereby highlighting areas for future research and improvement. The application of socially assistive robots for autism therapies is increasing. A recent study presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for ASD under an event-driven control scheme. Energy optimization is crucial as it allows therapists to extend and adapt sessions with autistic children, enhancing effectiveness during therapies. Finally, wearable assistive devices based on video camera technology pose unique challenges for visually impaired individuals. A proposed Tiny You Only Look Once (YOLO) architecture for pedestrian detection can be implemented in low-cost wearable devices, providing a potential solution for assistive technologies for the visually impaired. In conclusion, advancements in artificial intelligence and neural networks continue to revolutionize numerous fields, improving both the efficiency and quality of solutions. However, continued research and improvements are necessary to harness these technological breakthroughs and optimize their benefits. These five research papers illuminate the cutting-edge advancements and future potentials of artificial intelligence (AI) and neural networks in various fields, ranging from human activity recognition to medical image confidentiality, prosthetics, assistive robots for autism spectrum disorder (ASD), and wearable devices for the visually impaired. Each paper individually contributes to the broader landscape of AI, offering innovative solutions to existing challenges and opening new avenues for exploration. They reveal the transformative power of AI in enhancing real-time applications, increasing computational efficiency, improving user interactions, ensuring data confidentiality, and optimizing energy management. Yet, collectively, they tell a more profound story of how interconnected these AI applications are and how common threads weave through them. They reinforce the notion that the evolution of AI is not happening in silos but is a holistic, intertwined process where advancements in one area can catalyze growth in others. As AI continues to develop, researchers, developers, and practitioners must consider this interconnectedness. This approach will allow us to fully realize the potential of AI, not only to solve individual problems but also to create systemic changes that ripple across sectors and applications. AI is not only a tool or a participant in the process but a fundamental shift in how we perceive, interact with, and shape our world. The progress discussed in these papers is inspiring, yet it is equally a reminder of the endless journey ahead in AI research. The opportunities for AI to further revolutionize healthcare, accessibility, and our daily interactions are vast, and our collective pursuit of these advancements is what will continue to drive progress in this exciting, transformative field.

Artificial intelligence (AI) and machine learning (ML) are transforming spine care by addressing diagnostics, treatment planning, and rehabilitation challenges. This study highlights advancements in precision medicine for spinal pathologies, leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms, enabling faster and more accurate detection of abnormalities. AI-powered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries. Wearable devices and virtual platforms, designed with AI, offer personalized, adaptive therapies that improve treatment adherence and recovery outcomes. AI also enables preventive interventions by assessing spine condition risks early. Despite progress, challenges remain, including limited healthcare datasets, algorithmic biases, ethical concerns, and integration into existing systems. Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care. Future developments include multimodal AI systems integrating imaging, clinical, and genetic data for holistic treatment approaches. AI and ML promise significant improvements in diagnostic accuracy, treatment personalization, service accessibility, and cost efficiency, paving the way for more streamlined and effective spine care, ultimately enhancing patient outcomes.

China will establish the Xihe System based on Beidou/GNSS, Mobile Communication, Internet and Satellite Communication system to realize the Seamless Outdoor and Indoor Positioning in Wide Area (SOIPWA) on personal mobile terminals. Indoor Positioning System (IPS) is one of the main components and effective supplement for indoor positioning signal resource to resolve the positioning signal coverage of blind areas in complicated indoor environment based on current 2G/3G communication network and existing equipment. According to the requirement, the IPS adopted the unified time-frequency reference which affects the accuracy of indoor positioning directly. This paper provides an acquisition method of high accuracy frequency source used in the indoor positioning signal supplement. Positioning signals from outdoor base stations are acquired by RF front end and tracked by the carrier loop and code loop adjusting. After that, positioning signals are locked and synchronized to extract the high accuracy frequency source used for IPS, which can be built with low cost and precisely integrated with an outdoor positioning system. In practical test, the method satisfies the need of the accuracy and stability in time-frequency reference for IPS with high frequency accuracy, quick start and low drift rate through adjusting high accuracy Voltage Controlled Oscillator (VCO) by Digital to Analog Converter (DAC) with the frequency difference result from the adaptive loop.

In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human-computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human-machine interaction and health monitoring.

This article explores the impacts of digital transformations and new technologies in industrial sector (particularly through the Fourth Industrial Revolution) on optimizing production processes. Characterized by key technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), blockchains, and advanced robotics, Industry 4.0 has significantly shaped modern manufacturing management. IoT enables autonomous communications between machines and equipment, providing real-time insights into production parameters and enabling predictive maintenance, and big data plays a vital role by analyzing the large volumes of data that are generated by these devices, thus supporting informed management decisions. AI and machine learning help automate complex tasks, optimize production schedules, and improve product quality through real-time adjustments. Blockchain enables decentralized and secure data recording, which is particularly useful in supply-chain management. Advanced robotics increases production speed and accuracy, thus reducing labor costs and mitigating any risks that are associated with hazardous tasks. Integrating these technologies requires strategic planning, including identifying key challenges, conducting pilot projects, integrating with existing IT and OT systems, and managing organizational change. Measuring the effectiveness of Industry 4.0 implementation should involve well-defined key performance indicators (KPIs) and return-on-investment (ROI) analysis. The primary challenges that are associated with adopting Industry 4.0 include the alignment of technology with specific business needs, employee resistance to change, and hidden costs of implementation. In summary, industrial transformation offers opportunities for companies to optimize production processes, reduce costs, and increase competitiveness in the global marketplace. However, a careful approach is necessary to maximize efficiency, foster innovation, and secure long-term success in an increasingly digitalized world.

Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)-powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems. This scoping review aims to provide an overview of AI-powered wearable devices used for sleep disorders, focusing on study characteristics, wearable technology features, and AI methodologies for detection and analysis. Seven electronic databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus) were searched for peer-reviewed literature published before March 2024. Keywords were selected based on 3 domains: sleep disorders, AI, and wearable devices. The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. Study selection was conducted in 2 steps: first, by reviewing titles and abstracts, followed by full-text screening. Two reviewers independently conducted study selection and data extraction, resolving discrepancies by consensus. The extracted data were synthesized using a narrative approach. The initial search yielded 615 articles, of which 46 met the eligibility criteria and were included in the final analysis. The majority of studies focused on sleep apnea. Wearable AI was widely deployed for diagnosing and screening disorders; however, none of the studies used it for treatment. Commercial devices were the most commonly used type of wearable technology, appearing in 30 out of 46 (65%) studies. Among these, various brands were utilized rather than a single large, well-known brand; 19 (41%) studies used wrist-worn devices. Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). Wearable AI technology offers promising solutions for sleep disorders. These devices can be used for screening and diagnosis; however, research on wearable technology for sleep disorders other than sleep apnea remains limited. To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. Further research is necessary to validate machine learning techniques using clinical data from wearable devices and to develop useful analytics for data collection, monitoring, prediction, classification, and recommendation in the context of sleep disorders.