AI Concepts for System of Systems Dynamic Interoperability.

Interoperability is a central problem in digitization and System of Systems (SoS) engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cyber-physical systems (CPSs) at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as SoS, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish SoSs with purposeful CPS communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with artificial intelligence (AI)-enabled solutions in relation to SoS interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex SoSs, and realistic testbeds are needed.

Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning.

The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT.

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.

The Hidden Threat: Endocrine Disruptors and Their Impact on Insulin Resistance.

The association between Insulin resistance, a global health issue, and endocrine disruptors (EDCs), chemicals interfering with the endocrine system, has sparked concern in the scientific community. This article provides a comprehensive review of the existing literature regarding the intricate relationship between EDCs and insulin resistance. Phthalates, commonly found in consumer products, are well-established EDCs with documented effects on insulin-signaling pathways and metabolic processes. Epidemiological studies have connected phthalate exposure to an increased risk of type 2 diabetes mellitus (T2DM). Perfluoroalkyl substances (PFAS), persistent synthetic compounds, have shown inconsistent associations with T2DM in epidemiological research. However, studies suggest that PFAS may influence insulin resistance and overall metabolic health, with varying effects depending on specific PFAS molecules and study populations. Bisphenol A (BPA), found in plastics and resins, has emerged as a concern for glucose regulation and insulin resistance. Research has linked BPA exposure to T2DM, altered insulin release, obesity, and changes in the mass and function of insulin-secreting ß-cells. Triclosan, an antibacterial agent in personal care products, exhibits gender-specific associations with T2DM risk. It may impact gut microbiota, thyroid hormones, obesity, and inflammation, raising concerns about its effects on metabolic health. Furthermore, environmental EDCs like polycyclic aromatic hydrocarbons, pesticides, and heavy metals have demonstrated associations with T2DM, insulin resistance, hypertension, and obesity. Occupational exposure to specific pesticides and heavy metals has been linked to metabolic abnormalities.

Unraveling the Complexity of Apert Syndrome: Genetics, Clinical Insights, and Future Frontiers.

Apert syndrome (AS), also known as type I acrocephalosyndactyly, is a rare congenital condition characterized by craniosynostosis resulting from missense mutations in the fibroblast growth factor receptor 2 (FGFR2) gene. This comprehensive review delves into AS, covering its clinical manifestations, genetics, diagnosis, medical management, psychosocial considerations, and future research directions. AS presents with distinct features, including a brachycephalic skull, midface hypoplasia, and limb anomalies such as syndactyly. It follows an autosomal dominant inheritance pattern with mutations in the FGFR2 gene. Prenatal diagnosis is possible through advanced imaging techniques and molecular testing. The multidisciplinary approach to AS management involves surgical interventions, orthodontics, and psychological support. Although no curative treatment exists, early interventions can significantly improve function and aesthetics. The quality of life for AS patients is influenced by psychosocial factors, necessitating comprehensive support for both patients and their families. Future research directions include gene therapy, understanding cellular responses to FGFR2 mutations, and addressing genetic heterogeneity. Collaborative efforts are vital to advancing knowledge about AS and its genetic underpinnings. Overall, this review serves as a valuable resource for healthcare professionals, educators, and researchers, contributing to a deeper understanding of AS and facilitating advancements in diagnosis and treatment.