Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Power system is vital to modern societies,while it is susceptible to hazard events.Thus,analyzing resilience characteristics of power system is important.The standard model of infrastructure resilience,the resilience triangle,has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades.However,the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves(e.g.,residual performance and duration of recovery).Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data.Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance.There is a dire dearth of empirical studies in the field,which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems.To address this gap,this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States.Through the use of unsupervised machine learning,we examined different curve archetypes,as well as the fundamental properties of each resilience curve archetype.The results show two primary archetypes for power grid resilience curves,triangular curves,and trapezoidal curves.Triangular curves characterize resilience behavior based on three fundamental properties:1.critical functionality threshold,2.critical functionality recovery rate,and 3.recovery pivot point.Trapezoidal archetypes explain resilience curves based on 1.duration of sustained function loss and 2.constant recovery rate.The longer the duration of sustained function loss,the slower the constant rate of recovery.The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

Revealing the linguistic and geographical disparities of public awareness to Covid-19 outbreak through social media

The Covid-19 has presented an unprecedented challenge to public health worldwide.However,residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts.This study analyzed the global Twitter data from January 1st to June 30th,2020,to answer two research questions.What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media?Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak?We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness.The lag correlations between awareness and health impacts were examined at global and country levels.Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh.Asian countries showed more disparities in awareness than European countries,and awareness in Eastern Europe was higher than in central Europe.Finally,the Ratio index had high correlations with global mortality rate,global case fatality ratio,and country-level mortality rate,with 21-31,35-42,and 13–18 leading days,respectively.This study yields timely insights into social media use in understanding human behaviors for public health research.

Central composite design for optimization and formulation of desulphurization of iron ore concentrate using atmospheric leaching process

Owing to the negative effects of sulphur in iron ore on steelmaking process and environment, a tank leaching process was performed in atmospheric conditions to remove the sulphur from the iron ore concentrate and simultaneously to transform sulphide minerals into useful by-products. To achieve desirable sulphur removal rate and efficiency, central composite design was adopted as a response surface methodology for the optimization and evaluation of the process. A full-quadratic polynomial equation between the sulphur removal and the studied parameters was established to assess the behaviour of sulphur removal as a function of the factors and to predict the results in various conditions. The optimum conditions were obtained based on the variance tests and response surface plots, from which the optimized ranges for each factor resulting in the best response （corresponding to the highest percentage of desulphurization） could be then achieved. The results show that most desirable conditions are atmospheric leaching in 1.39 mol/dm3 nitric acid and 0.88 mol/dm3 sulphuric acid for 47 h. The designed process under the optimized desulphurization conditions was applied to a real iron ore concentrate. More than 75% of the total sulphur was removed via the leaching process. In addition to the desulphurization, the conversion of sulphide-bearing minerals into useful by-products, extraction of valuable metals, and executing the process under atmospheric conditions are the other advantages of the proposed method.