Smart grids are expected to become an essential component of the future energy system. The technical potential of smart grids is far reaching and increasingly well understood, and smart grids are now in the early phas...Smart grids are expected to become an essential component of the future energy system. The technical potential of smart grids is far reaching and increasingly well understood, and smart grids are now in the early phases of market deployment in several regions, particularly, in Europe and the US. Less understood than the technical aspects is how and to what degree end users （i.e. the customers） are willing and able to embrace smart grid technologies and the changes in mindset associated with this transition. This article reports the main findings from an lEA （International Energy Agency）-DSM （demand side management） project addressing the role of customers in a smart grid deployment scheme, specifically how customer behavior may restrict the technical potential of smart grids from being realized. With a model of household energy behavior as the theoretical point of departure, the research builds on experiences from various smart grid pilot studies, together with consumer research within similar domains, to identify behavioral challenges that are likely to hamper adoption of＂smart grid behaviors＂. Based on this insight, a set of recommendations to minimize customer resistance to smart grid deployment is suggested.展开更多
Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases.Several computational algorithms have recently been developed for this purpose ...Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases.Several computational algorithms have recently been developed for this purpose by using transcriptome and network data.However,it remains largely unclear which algorithm performs better under a specific condition.Such knowledge is important for both appropriate application and future enhancement of these algorithms.Here,we systematically evaluated seven main algorithms(TED,TDD,TFactS,RIF1,RIF2,dCSA_t2t,and dCSA_r2t),using both simulated and real datasets.In our simulation evaluation,we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators.We found that all these algorithms could effectively discern signals arising from regulatory network differences,indicating the validity of our simulation schema.Among the seven tested algorithms,TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered.When applied to two independent lung cancer datasets,both TED and TFactS replicated a substantial fraction of their respective differential regulators.Since TED and TFactS rely on two distinct features of transcriptome data,namely differential co-expression and differential expression,both may be applied as mutual references during practical application.展开更多
文摘Smart grids are expected to become an essential component of the future energy system. The technical potential of smart grids is far reaching and increasingly well understood, and smart grids are now in the early phases of market deployment in several regions, particularly, in Europe and the US. Less understood than the technical aspects is how and to what degree end users （i.e. the customers） are willing and able to embrace smart grid technologies and the changes in mindset associated with this transition. This article reports the main findings from an lEA （International Energy Agency）-DSM （demand side management） project addressing the role of customers in a smart grid deployment scheme, specifically how customer behavior may restrict the technical potential of smart grids from being realized. With a model of household energy behavior as the theoretical point of departure, the research builds on experiences from various smart grid pilot studies, together with consumer research within similar domains, to identify behavioral challenges that are likely to hamper adoption of＂smart grid behaviors＂. Based on this insight, a set of recommendations to minimize customer resistance to smart grid deployment is suggested.
基金partially supported by US National Institutes of Health(R01LM011177,R03CA167695,P30CA68485,P50CA095103 and P50CA098131)Ingram Professorship Funds(to Zhao ZhongMing)The Robert J.Kleberg,Jr.and Helen C.Kleberg Foundation(to Zhao ZhongMing)
文摘Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases.Several computational algorithms have recently been developed for this purpose by using transcriptome and network data.However,it remains largely unclear which algorithm performs better under a specific condition.Such knowledge is important for both appropriate application and future enhancement of these algorithms.Here,we systematically evaluated seven main algorithms(TED,TDD,TFactS,RIF1,RIF2,dCSA_t2t,and dCSA_r2t),using both simulated and real datasets.In our simulation evaluation,we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators.We found that all these algorithms could effectively discern signals arising from regulatory network differences,indicating the validity of our simulation schema.Among the seven tested algorithms,TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered.When applied to two independent lung cancer datasets,both TED and TFactS replicated a substantial fraction of their respective differential regulators.Since TED and TFactS rely on two distinct features of transcriptome data,namely differential co-expression and differential expression,both may be applied as mutual references during practical application.
