Variable-fidelity surrogate modeling offers an efficient way to generate aerodynamic data for aero-loads prediction based on a set of CFD methods with varying degree of fidelity and computational ...expense. In this paper, direct Gradient-Enhanced Kriging (GEK) and a newly developed Generalized Hybrid Bridge Function (GHBF) have been combined in order to improve the efficiency and accuracy of the existing Variable-Fidelity Modeling (VFM) approach. The new algorithms and features are demonstrated and evaluated for analytical functions and are subsequently used to construct a global surrogate model for the aerodynamic coefficients and drag polar of an RAE 2822 airfoil. It is shown that the gradient-enhanced GHBF proposed in this paper is very promising and can be used to significantly improve the efficiency, accuracy and robustness of VFM in the context of aero-loads prediction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Seed yield and oil content are two important agricultural characteristics in oil crop breeding, and a lot of functional gene research is being concentrated on increasing these factors. In this study, ...by differential gene expression analyses between rapeseed lines (zy036 and 51070) which exhibit different levels of seed oil production, BnGRF2 (Brassica napus growth-regulating factor 2-like gene) was identified in the high oil-producing line zy036. To elucidate the possible roles of BnGRF2 in seed oil production, the cDNA sequences of the rapeseed GRF2 gene were isolated. The Blastn result showed that rapeseed contained BnGRF2a/2b which were located in the A genome (A1 and A3) and C genome (C1 and C6), respectively, and the dominantly expressed gene BnGRF2a was chosen for transgenic research. Analysis of 35S-BnGRF2a transgenic Arabidopsis showed that overexpressed BnGRF2a resulted in an increase in seed oil production of >50%. Moreover, BnGRF2a also induced a >20% enlargement in extended leaves and >40% improvement in photosynthetic efficiency because of an increase in the chlorophyll content. Furthermore, transcriptome analyses indicated that some genes associated with cell proliferation, photosynthesis, and oil synthesis were up-regulated, which revealed that cell number and plant photosynthesis contributed to the increased seed weight and oil content. Because of less efficient self-fertilization induced by the longer pistil in the 35S-BnGRF2a transgenic line, Napin-BnGRF2a transgenic lines were further used to identify the function of BnGRF2, and the results showed that seed oil production also could increase >40% compared with the wild-type control. The results suggest that improvement to economically important characteristics in oil crops may be achieved by manipulation of the GRF2 expression level.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Although learners' success in learning has generally been attributed to cognitive factors, non-cognitive issues in education should be taken into consideration in the process of learning which ...affects learners' achievement. One of these issues, which become popular among researchers in the previous decade is grit, that is, posited as passion and perseverance thanks to its enduring quality and the other is self-efficacy. Another factor is goal commitment that talks about the way to reach a goal or insistent determinations to achieve a goal. The proposed review attempts to focus on these three factors in regulating students' learning achievement. Accordingly, some educational suggestions are offered for teachers, students, and syllabus designers.
Quantum computational advantage using photons Zhong, Han-Sen; Wang, Hui; Deng, Yu-Hao ...
Science (American Association for the Advancement of Science),
12/2020, Volume:
370, Issue:
6523
Journal Article
Peer reviewed
Open access
Quantum computers promise to perform certain tasks that are believed to be intractable to classical computers. Boson sampling is such a task and is considered a strong candidate to demonstrate the ...quantum computational advantage. We performed Gaussian boson sampling by sending 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix-the whole optical setup is phase-locked-and sampling the output using 100 high-efficiency single-photon detectors. The obtained samples were validated against plausible hypotheses exploiting thermal states, distinguishable photons, and uniform distribution. The photonic quantum computer,
, generates up to 76 output photon clicks, which yields an output state-space dimension of 10
and a sampling rate that is faster than using the state-of-the-art simulation strategy and supercomputers by a factor of ~10
.
Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' ...binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.
The use of Jiuqu as a saccharifying and fermenting starter in the production of fermented foods is a very old biotechnological process that can be traced back to ancient times. Jiuqu harbors a hub of ...microbial communities, in which prokaryotes and eukaryotes cohabit, interact, and communicate. However, the spontaneous fermentation based on empirical processing hardly guarantees the stable assembly of the microbiome and a standardized quality of Jiuqu. This review describes the state of the art, limitations, and challenges towards the application of traditional and omics‐based technology to study the Jiuqu microbiome and highlights the need for integrating meta‐omics data. In addition, we review the varieties of Jiuqu and their production processes, with particular attention to factors shaping the microbiota of Jiuqu. Then, the potentials of integrated omics approaches used in Jiuqu research are examined in order to understand the assembly of the microbiome and improve the quality of the products. A variety of different approaches, including molecular and mass spectrometry‐based techniques, have led to scientific advances in the analysis of the complex ecosystem of Jiuqu. To date, the extensive research on Jiuqu has mainly focused on the microbial community diversity, flavor profiles, and biochemical characteristics. An integrative approach to large‐scale omics datasets and cultivated microbiota has great potential for understanding the interrelation of the Jiuqu microbiome. Further research on the Jiuqu microbiome may explain the inherent property of compositional stability and stable performance of a complex microbiota coping with environmental perturbations and provide important insights to reconstruct synthetic microbiota and develop modern intelligent manufacturing procedures for Jiuqu.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
We report phase-programmable Gaussian boson sampling (GBS) which produces up to 113 photon detection events out of a 144-mode photonic circuit. A new high-brightness and scalable quantum light source ...is developed, exploring the idea of stimulated emission of squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. The obtained samples are efficiently validated by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our GBS experiment passes a nonclassicality test based on inequality constraints, and we reveal nontrivial genuine high-order correlations in the GBS samples, which are evidence of robustness against possible classical simulation schemes. This photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to ∼ 1043, and a sampling rate ∼ 1024 faster than using brute-force simulation on classical supercomputers.
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CMK, CTK, FMFMET, IJS, NUK, PNG, UL, UM
We study the suppressions of high transverse momentum single hadron and dihadron productions in high-energy heavy-ion collisions based on the framework of a next-to-leading-order perturbative QCD ...parton model combined with the higher-twist energy loss formalism. Our model can provide a consistent description for the nuclear modification factors of single hadron and dihadron productions in central and non-central nucleus–nucleus collisions at RHIC and the LHC energies. We quantitatively extract the value of jet quenching parameter
q
^
via a global
χ
2
analysis, and obtain
q
^
/
T
3
=
4.1
–4.4 at
T
=
378
MeV at RHIC and
q
^
/
T
3
=
2.6
–3.3 at
T
=
486
MeV at the LHC, which are consistent with the results from JET Collaboration. We also provide the predictions for the nuclear modification factors of dihadron productions in Pb + Pb collisions at
s
NN
= 5.02 TeV and in Xe + Xe collisions at
s
NN
= 5.44 TeV.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The efficient global optimization method (EGO) based on kriging surrogate model and expected improvement (EI) has received much attention for optimization of high-fidelity, expensive functions. ...However, when the standard EI method is directly applied to a variable-fidelity optimization (VFO) introducing assistance from cheap, low-fidelity functions via hierarchical kriging (HK) or cokriging, only high-fidelity samples can be chosen to update the variable-fidelity surrogate model. The theory of infilling low-fidelity samples towards the improvement of high-fidelity function is still a blank area. This article proposes a variable-fidelity EI (VF-EI) method that can adaptively select new samples of both low and high fidelity. Based on the theory of HK model, the EI of the high-fidelity function associated with adding low- and high-fidelity sample points are analytically derived, and the resulting VF-EI is a function of both the design variables
x
and the fidelity level
l
. Through maximizing the VF-EI, both the sample location and fidelity level of next numerical evaluation are determined, which in turn drives the optimization converging to the global optimum of high-fidelity function. The proposed VF-EI is verified by six analytical test cases and demonstrated by two engineering problems, including aerodynamic shape optimizations of RAE 2822 airfoil and ONERA M6 wing. The results show that it can remarkably improve the optimization efficiency and compares favorably to the existing methods.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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