Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Strain engineering of graphene: a review

Abstract: Graphene has intrigued the science community by many unique properties not found in conventional materials. In particular, it is the strongest two-dimensional material ever measured, being able to sustain reversible tensile elastic strain larger than 20%, which yields an interesting possibility to tune the properties of graphene by strain and thus opens a new field called "straintronics". In this article, the current progress in the strain engineering of graphene is reviewed. We first summarize the strain effects on the electronic structure and Raman spectra of graphene. We then highlight the electron-phonon coupling greatly enhanced by the biaxial strain and the strong pseudomagnetic field induced by the non-uniform strain with specific distribution. Finally, the potential application of strain-engineering in the self-assembly of foreign atoms on the graphene surface is also discussed. Given the short history of graphene straintronics research, the current progress has been notable, and many further advances in this field are expected.

Surface plasmon resonance-mediated photocatalysis by noble metal-based composites under visible light

Abstract: Harvesting abundant and renewable sunlight in energy production and environmental remediation is an emerging research topic. Indeed, research on solar-driven heterogeneous photocatalysis based on surface plasmon resonance has seen rapid growth and potentially opens a technologically promising avenue that can benefit the sustainable development of global energy and the environment. This review briefly summarizes recent advances in the synthesis and photocatalytic properties of plasmonic composites (e.g., hybrid structures) formed by noble metal (e.g., gold, silver) nanoparticles dispersed on a variety of substrates that are composed of metal oxides, silver halides, graphene oxide, among others. Brief introduction of surface plasmon resonance and the synthesis of noble metal-based composites are given, followed by highlighting diverse applications of plasmonic photocatalysts in mineralization of organic pollutants, organic synthesis and water splitting. Insights into surface plasmon resonance-mediated photocatalysis not only impact the basic science of heterogeneous photocatalysis, but generate new concepts guiding practical technologies such as wastewater treatment, air purification, selective oxidation reactions, selective reduction reactions, and solar-to-hydrogen energy conversion in an energy efficient and environmentally benign approach. This review ends with a summary and perspectives.

Electron and Positron Fluxes in Primary Cosmic Rays Measured with the Alpha Magnetic Spectrometer on the International Space Station

Abstract: Precision measurements by the Alpha Magnetic Spectrometer on the International Space Station of the primary cosmic-ray electron flux in the range 0.5 to 700 GeV and the positron flux in the range 0.5 to 500 GeV are presented. The electron flux and the positron flux each require a description beyond a single power-law spectrum. Both the electron flux and the positron flux change their behavior at ∼30 GeV but the fluxes are significantly different in their magnitude and energy dependence. Between 20 and 200 GeV the positron spectral index is significantly harder than the electron spectral index. The determination of the differing behavior of the spectral indices versus energy is a new observation and provides important information on the origins of cosmic-ray electrons and positrons.

Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling.

Abstract: This paper investigates the dependence of synchronization transitions of bursting oscillations on the information transmission delay over scale-free neuronal networks with attractive and repulsive coupling. It is shown that for both types of coupling, the delay always plays a subtle role in either promoting or impairing synchronization. In particular, depending on the inherent oscillation period of individual neurons, regions of irregular and regular propagating excitatory fronts appear intermittently as the delay increases. These delay-induced synchronization transitions are manifested as well-expressed minima in the measure for spatiotemporal synchrony. For attractive coupling, the minima appear at every integer multiple of the average oscillation period, while for the repulsive coupling, they appear at every odd multiple of the half of the average oscillation period. The obtained results are robust to the variations of the dynamics of individual neurons, the system size, and the neuronal firing type. Hence, they can be used to characterize attractively or repulsively coupled scale-free neuronal networks with delays.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.

Abstract: Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.