Harvard University

About: Harvard University is a education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 208150 authors who have published 530388 publications receiving 38152182 citations. The organization is also known as: Harvard & University of Harvard.

Controllability of complex networks

Abstract: The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network's degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.

Pattern and growth in personality

Abstract: Pattern and growth in personality , Pattern and growth in personality , کتابخانه دیجیتال و فن آوری اطلاعات دانشگاه امام صادق(ع)

Coaxial silicon nanowires as solar cells and nanoelectronic power sources

Abstract: Solar cells are attractive candidates for clean and renewable power; with miniaturization, they might also serve as integrated power sources for nanoelectronic systems. The use of nanostructures or nanostructured materials represents a general approach to reduce both cost and size and to improve efficiency in photovoltaics. Nanoparticles, nanorods and nanowires have been used to improve charge collection efficiency in polymer-blend and dye-sensitized solar cells, to demonstrate carrier multiplication, and to enable low-temperature processing of photovoltaic devices. Moreover, recent theoretical studies have indicated that coaxial nanowire structures could improve carrier collection and overall efficiency with respect to single-crystal bulk semiconductors of the same materials. However, solar cells based on hybrid nanoarchitectures suffer from relatively low efficiencies and poor stabilities. In addition, previous studies have not yet addressed their use as photovoltaic power elements in nanoelectronics. Here we report the realization of p-type/intrinsic/n-type (p-i-n) coaxial silicon nanowire solar cells. Under one solar equivalent (1-sun) illumination, the p-i-n silicon nanowire elements yield a maximum power output of up to 200 pW per nanowire device and an apparent energy conversion efficiency of up to 3.4 per cent, with stable and improved efficiencies achievable at high-flux illuminations. Furthermore, we show that individual and interconnected silicon nanowire photovoltaic elements can serve as robust power sources to drive functional nanoelectronic sensors and logic gates. These coaxial silicon nanowire photovoltaic elements provide a new nanoscale test bed for studies of photoinduced energy/charge transport and artificial photosynthesis, and might find general usage as elements for powering ultralow-power electronics and diverse nanosystems.

Forecasting Using Principal Components From a Large Number of Predictors

Abstract: This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large. The estimated factors are shown to be consistent, even in the presence of time variation in the factor model.