Current research on Network Digital Twins (NDT) focuses on building models that predict the behavior of the network on simulations rather than building a complete NDT that automatically monitors a physical network and optimizes it according to its current state and traffic flows. In this paper, we implement an online learning NDT for Interior Gateway Protocol (IGP) weight optimization that monitors a physical network to find IGP weight configurations based on a selected objective, such as minimizing average traffic delay, and automatically applies them to the network. We addressed the practical issues of implementing a NDT on a real network, such as detecting and dealing with an improperly trained digital model of the network. Our results indicate that our NDT can effectively optimize a physical testbed and that fine-tuning its digital model on high traffic scenarios improves the performance of the NDT on a small network.

Current research on Network Digital Twins (NDT) focuses on building models that predict the behavior of the network on simulations rather than building a complete NDT that automatically monitors a physical network and optimizes it according to its current state and traffic flows. In this proposal, we demo a NDT for Interior Gateway Protocol (IGP) weight optimization on a physical network of 7 Cisco routers and an IXIA generator connected via Ethernet links. We will show how a link failure in this network results in a large packet loss, and how our NDT automatically optimizes the IGP weights of the network to reduce packet loss. Our demo presents a practical implementation of a NDT, where the audience can see a self-adapting network without human interference.

OBJECTIVE: To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions. MATERIALS AND METHODS: We performed a peer-matched controlled cohort study from March to September 2022 to evaluate the impact of DAX in outpatient clinics in an integrated healthcare system. Primary outcome measurements included provider engagement survey results, reported patient safety events related to DAX use, patients' Likelihood to Recommend score, number of patients opting out of ambient listening, change in work relative values units, attributed value-based primary care panel size, documentation completion and CPT code submission deficiency rates, and note turnaround time. RESULTS: A total of 99 providers representing 12 specialties enrolled in the study; 76 matched control group providers were included for analysis. Median utilization of DAX was 47% among active participants. We found positive trends in provider engagement, while non-participants saw worsening engagement and no practical change in productivity. There was a statistically significant worsening of after-hours EHR. There was no quantifiable effect on patient safety. DISCUSSION: Nuance DAX use showed positive trends in provider engagement at no risk to patient safety, experience, or clinical documentation. There were no significant benefits to patient experience, documentation, or measures of provider productivity. CONCLUSION: Our results highlight the potential of ambient dictation as a tool for improving the provider experience. Head-to-head comparisons of EHR documentation efficiency training are needed.

Accurate short-term wind forecasts (STWFs), with time horizons from 0.5 to 6 hours, are essential for efficient integration of wind power to the electrical power grid. Physical models based on numerical weather predictions are currently not competitive, and research on machine learning approaches is ongoing. Two major challenges confronting these efforts are missing observations and weather-regime induced dependency shifts among wind variables at geographically distributed sites. In this paper we introduce approaches that address both of these challenges. We describe a new regime-aware approach to STWF that use auto-regressive hidden Markov models (AR-HMM), a subclass of conditional linear Gaussian (CLG) models. Although AR-HMMs are a natural representation for weather regimes, as with CLG models in general, exact inference is NP-hard when observations are missing (Lerner and Parr, 2001). Because of this high cost, we introduce a simple approximate inference method for AR-HMMs, which we believe has applications to other sequential and temporal problem domains that involve continuous variables. In an empirical evaluation on publicly available wind data from two geographically distinct regions, our approach makes significantly more accurate predictions than baseline models, and uncovers meteorologically relevant regimes.