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Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.


Papers
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TL;DR: This paper is the first to reveal the five keyword-constructed schemes, research hotspots, and development trends of the smart city, Construction 4.0, Industry 4.1, BIM, and sustainable construction, from 2014 to 2021 (a period of eight years).
Abstract: At present, the smart city offers the most desired state of urban development, encompassing, as it does, the concept of sustainable development. The creation of a smart city is closely associated with upgrading the construction industry to encompass many emerging concepts and technologies, such as Construction 4.0, with its roots in Industry 4.0, and the deployment of building information modeling (BIM) as an essential tool for the construction industry. Therefore, this paper aims to explore the current state of the art and development trajectory of the multidisciplinary integration of Construction 4.0, Industry 4.0, BIM, and sustainable construction in the context of the smart city. It is the first attempt in the literature to use both macro-quantitative analysis and micro-qualitative analysis methods to investigate this multidisciplinary research topic. By using the visual bibliometric tool, VOSviewer, and based on macro keyword co-occurrence, this paper is the first to reveal the five keyword-constructed schemes, research hotspots, and development trends of the smart city, Construction 4.0, Industry 4.0, BIM, and sustainable construction, from 2014 to 2021 (a period of eight years). Additionally, the top 11 productive subject areas have been identified with the help of VOSviewer software keyword-clustering analysis and application. Furthermore, the whole-building life cycle is considered as an aid to identifying research gaps and trends, providing suggestions for future research with the assistance of an upgraded version of BIM, namely, city information modeling (CIM) and the future integration of Industry 5.0 and Construction 5.0, or even of Industry Metaverse with Construction Metaverse.

20 citations

Journal ArticleDOI
TL;DR: A highly efficient blue emitter TPE-4Py with an aggregation-induced emission (AIE) effect is achieved by combining twisted tetraphenylethene (TPE) core and planar pyrene peripheries and shows the bifunctional property as both an emitter and a hole-transporting layer.
Abstract: Organic luminogens with strong solid-state emission have attracted much attention for their widely practical applications. However, the traditional organic luminogens with planar conformations often suffer from the notorious aggregation-caused quenching (ACQ) effect in solid state for the π–π stacking. Here, a highly efficient blue emitter TPE-4Py with an aggregation-induced emission (AIE) effect is achieved by combining twisted tetraphenylethene (TPE) core and planar pyrene peripheries. When the emitter was spin-coated in non-doped OLEDs with or without a hole-transporting layer, comparable EL performance was achieved, showing the bifunctional property as both an emitter and a hole-transporting layer. Furthermore, its EL efficiency was promoted in doped OLED, even at a high doping concentration (50%), because of its novel AIE effect, with a current efficiency up to 4.9 cd/A at 484 nm.

20 citations

Journal ArticleDOI
TL;DR: In this paper, the authors report a comprehensive theoretical study on reaction of methane by Fe4 cluster and show that the cleavage of the first C-H bond is both an energetically and kinetically favorable process and the breaking of the second C−H is the rate-determining step.

20 citations

Journal ArticleDOI
TL;DR: In this paper, the structural and field emission properties of CuO nanoribbons decorated with Ag nanoparticles by a wet chemical method were investigated, and the results demonstrated a remarkable enhancement of field emission performance.

20 citations


Cited by
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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations