Parametric Macromodels of Drivers for SSN Simulations

This paper addresses the modeling of output and power supply ports of digital drivers for accurate and efficient SSN simulations. The proposed macromodels are defined by parametric relations, whose parameters are estimated from measured or simulated port transient responses, and are implemented as SPICE subcircuits. The modeling technique is applied to commercial high-speed devices and a realistic simulation example is shown

Behavioral modeling of digital IC input and output ports

This paper addresses the development of accurate and efficient behavioral models of digital integrated circuit input and output ports for signal integrity simulations and timing analyses. The modeling process is described and applied to the characterization of actual device

Behavioral Modeling of IC Ports Including Temperature Effects

The development of temperature-dependent macromodels for digital IC ports is addressed. The proposed modeling approach is based on the theory of discrete-time parametric models and allows one to estimate the model parameters from voltage and current waveforms observed at the ports and to implement the model as a SPICE subcircuit. The proposed technique is validated by applying it to commercial devices described by detailed transistor-level models. The obtained models perform at a good accuracy level and are more efficient than the original transistor-level models

Monetary Policy and Corporate Bond Returns

We investigate the impact of monetary policy shocks (the surprise change in the Fed Funds rate (FFR)) on excess corporate bonds returns. We obtain a significant negative response of bond returns to FFR shocks. This effect is especially strong in the period before the 2007- 09 financial crisis and for bonds with longer maturity and lower rating. We show that the largest portion of this response is related to higher expected excess bond returns, especially term premia news. Therefore, the discount-rate channel represents an important mechanism through which monetary policy affects corporate bonds. However, the financial crisis has attenuated this effect

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED)

Macromodeling strategy for digital devices and interconnects

International audienceThis paper proposes a macromodeling approach for the simulation of digital interconnected systems. Such an approach is based on a set of macromodels describing IC ports, IC packages and multiconductor interconnect structures in standard circuit simulators, like SPICE. We illustrate the features of the macromodels and we demonstrate the proposed approach on a realistic simulation problem

Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

OBJECTIVES: Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN: Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA: Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527 458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES: Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS: Of the 527 458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49 486 children (9.4%) of the population were classified in the \u27At Higher Risk\u27 group using a threshold of predicted risk \u3e2.5%. The observed risk of hospitalisation (5%) for the \u27At Higher Risk\u27 group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS: It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients\u27 likelihood of a preventable, high-cost hospitalisation

Assessing Chronic Disease Rates Through Automated Pharmacy Data

No abstract available

Parametric Macromodels of Drivers for SSN Simulations

This paper addresses the modeling of output and power supply ports of digital drivers for accurate and efficient SSN simulations. The proposed macromodels are defined by parametric relations, whose parameters are estimated from measured or simulated port transient responses, and are implemented as SPICE subcircuits. The modeling technique is applied to commercial high-speed devices and a realistic simulation example is shown