RESUMEN
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.
RESUMEN
Gestational diabetes mellitus (GDM) is today universally diagnosed during late pregnancy. Treating hyperglycaemia during pregnancy reduces the risk of complications, the effect of interventions is however limited due to the late diagnosis. It is thus important to identify biomarkers reaching a high precision for GDM development in early pregnancy. Here we aim to investigate soluble CD163 (sCD163) and soluble tumour necrosis factor-like weak inducer of apoptosis (sTWEAK) in early pregnancy GDM and their association to the development of later glucose intolerance. In this case-control study, women diagnosed with GDM in early pregnancy (n = 70) at Lund University Hospital, Lund, Sweden in 2011-2015 were age- and BMI matched to pregnant volunteers without diabetes (n = 70) recruited in early pregnancy from maternal health care centres in 2014-2015. Plasma levels of sCD163 and sTWEAK were analysed using commercial ELISA. Plasma levels of sCD163 did not differ between patients with and without GDM in early pregnancy (p = 0.86), plasma levels of sTWEAK however was decreased in women with GDM (0.71 [0.4-1.75] ng/ml) compared to controls (1.38 [0.63-4.86] ng/ml; p = 0.003). Women with sTWEAK levels in the lowest tertile had an increased risk of GDM in early pregnancy (p = 0.014). Neither sCD163 nor sTWEAK were associated with later glucose intolerance in women with GDM. This study reports decreased levels of sTWEAK in women with early pregnancy GDM, independent of age and BMI. Neither sCD163 nor sTWEAK were found to be associated to later glucose intolerance.