| 101. |
- Lian, Ruqian, et al.
(författare)
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Q-Carbon : A New Carbon Allotrope with a Low Degree of s-p Orbital Hybridization and Its Nucleation Lithiation Process in Lithium-Ion Batteries
- 2020
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Ingår i: ACS Applied Materials and Interfaces. - : AMER CHEMICAL SOC. - 1944-8244 .- 1944-8252. ; 12:1, s. 619-626
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Tidskriftsartikel (refereegranskat)abstract
- A novel metallic carbon allotrope, Q-carbon, was discovered using first-principles calculations. The named Q-carbon possessed a three-dimensional (3D) cage structure formed by carbon atoms with three ligands. The energy distribution of electrons in different orbitals revealed that Q-carbon has a low degree of s-p orbital hybridization. The calculated Li+ binding energies suggested Li+ aggregation inside Q-carbon during lithiation. As a result, a Li8C32 phase was formed and gradually expanded in Q-carbon, implying a typical two-phase transition. This allowed Q-carbon to have a constant theoretical voltage of 0.40 V, which effectively inhibited Li dendrite formation. A stable Li8C32/C-32 two-phase interface was confirmed by stress-strain analysis, and a calculated Li+ diffusion barrier of similar to 0.50 eV ensured effective Li+ diffusion along a 3D pathway. This study was of great significance for the understanding of two-phase transition of Li+ storage materials and provided a new insight into the design of new carbon materials for energy storage applications.
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| 102. |
- Lin, Meichao, et al.
(författare)
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A predictive model unifying hydrogen enhanced plasticity and decohesion
- 2022
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Ingår i: Scripta Materialia. - : Elsevier. - 1359-6462 .- 1872-8456. ; 215
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Tidskriftsartikel (refereegranskat)abstract
- The detrimental effect of hydrogen on metals which manifests itself as a transition from a ductile to a brittle failure mode is, for the first time, incorporated into a unified continuum-scale predictive framework. The complete Gurson model, designed to predict ductile failure by voiding, is extended to include failure by deco-hesion. Hydrogen enhanced plasticity is accounted for through acceleration of the voiding process while hydrogen induced decohesion is realized by a degradation of the decohesion threshold. The interplay between these two failure modes driven by hydrogen concentration are well captured. This model can predict a realistic level of embrittlement as well as the suppression of dimples in a hydrogen induced fracture surface. Being generic, versatile, and easy to implement, the model may serve as a basis for interpretation of laboratory ex-periments and enable the transferability of the laboratory results to the integrity assessment of engineering components in hydrogen environment.
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| 103. |
- Liu, Di, et al.
(författare)
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EDF-VD scheduling of mixed-criticality systems with degraded quality guarantees
- 2016
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Ingår i: Proc. 37th Real-Time Systems Symposium. - : IEEE Computer Society. - 9781509053032 ; , s. 35-46
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Konferensbidrag (refereegranskat)abstract
- This paper studies real-time scheduling of mixed-criticality systems where low-criticality tasks are still guaranteed some service in the high-criticality mode, with reduced execution budgets. First, we present a utilization-based schedulability test for such systems under EDF-VD scheduling. Second, we quantify the suboptimality of EDF-VD (with our test condition) in terms of speedup factors. In general, the speedup factor is a function with respect to the ratio between the amount of resource required by different types of tasks in different criticality modes, and reaches 4/3 in the worst case. Furthermore, we show that the proposed utilization-based schedulability test and speedup factor results apply to the elastic mixed-criticality model as well. Experiments show effectiveness of our proposed method and confirm the theoretical suboptimality results.
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| 104. |
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| 105. |
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| 106. |
- Liu, Gang, et al.
(författare)
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Evaluating the reintroduction project of Przewalski's horse in China using genetic and pedigree data
- 2014
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Ingår i: Biological Conservation. - : Elsevier BV. - 0006-3207 .- 1873-2917. ; 171, s. 288-298
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Tidskriftsartikel (refereegranskat)abstract
- Przewalski's horse went extinct in the wild in the mid 1960s. Starting in 1985, individuals were brought from western zoos to two centers in China and breeding programs were initiated. With the increasing size of captive populations, two reintroduction projects were launched in the northwestern China in 2001 and 2010. Knowledge on genetic diversity in China's horse populations is limited, but would help improve the genetic management and assess the success of the reintroduction. Accordingly, one reintroduced and two captive populations were examined with 10 microsatellite loci together with pedigree data. The results showed higher level of diversity within the captive populations than the reintroduced population, indicating some alleles may have been lost during reintroduction. Genetic differentiation was detected among populations (F-ST = 0.09 +/- 0.05, Rhos(ST) = 0.05 +/- 0.02) and Bayesian clustering supported the presence of three subpopulations. The highest genetic differentiation was observed between the captive and reintroduced populations, and inbreeding coefficients were generally higher in the reintroduced population. Temporal estimates of both pedigree and microsatellite data showed a high, but decreasing level inbreeding. Through simulations, we estimated that the reintroduced population needs more than 100 individuals to retain approximately 90% of its current, already depauperate, genetic diversity. We have provided recommendations for the management program concerning introgressed genes from domestic horse and the number and origin of individuals for future reintroductions. (C) 2014 Elsevier Ltd. All rights reserved.
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| 107. |
- Liu, Hui, et al.
(författare)
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Repetitive elements, sequence turnover and cyto-nuclear gene transfer in gymnosperm mitogenomes
- 2022
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Ingår i: Frontiers in Genetics. - : Frontiers Media S.A.. - 1664-8021. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Among the three genomes in plant cells, the mitochondrial genome (mitogenome) is the least studied due to complex recombination and intergenomic transfer. In gymnosperms only ∼20 mitogenomes have been released thus far, which hinders a systematic investigation into the tempo and mode of mitochondrial DNA evolution in seed plants. Here, we report the complete mitogenome sequence of Platycladus orientalis (Cupressaceae). This mitogenome is assembled as two circular-mapping chromosomes with a size of ∼2.6 Mb and which contains 32 protein-coding genes, three rRNA and seven tRNA genes, and 1,068 RNA editing sites. Repetitive sequences, including dispersed repeats, transposable elements (TEs), and tandem repeats, made up 23% of the genome. Comparative analyses with 17 other mitogenomes representing the five gymnosperm lineages revealed a 30-fold difference in genome size, 80-fold in repetitive content, and 230-fold in substitution rate. We found dispersed repeats are highly associated with mitogenome expansion (r = 0.99), and most of them were accumulated during recent duplication events. Syntenic blocks and shared sequences between mitogenomes decay rapidly with divergence time (r = 0.53), with the exceptions of Ginkgo and Cycads which retained conserved genome structure over long evolutionary time. Our phylogenetic analysis supports a sister group relationship of Cupressophytes and Gnetophytes; both groups are unique in that they lost 8–12 protein-coding genes, of which 4–7 intact genes are likely transferred to nucleus. These two clades also show accelerated and highly variable substitution rates relative to other gymnosperms. Our study highlights the dynamic and enigmatic evolution of gymnosperm mitogenomes.
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| 108. |
- Liu, Jun, et al.
(författare)
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Feature Boosting Network for 3D Pose Estimation
- 2020
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 42:2, s. 494-501
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.
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| 109. |
- Liu, Jun, et al.
(författare)
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NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
- 2020
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 42:10, s. 2684-2701
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Tidskriftsartikel (refereegranskat)abstract
- Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.
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| 110. |
- Liu, Jun, et al.
(författare)
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Skeleton-Based Online Action Prediction Using Scale Selection Network
- 2020
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 42:6, s. 1453-1467
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Tidskriftsartikel (refereegranskat)abstract
- Action prediction is to recognize the class label of an ongoing activity when only a part of it is observed. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis. Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step. An activation sharing scheme is also proposed to handle the overlapping computations among the adjacent time steps, which enables our framework to run more efficiently. Moreover, to enhance the performance of our framework for action prediction with the skeletal input data, a hierarchy of dilated tree convolutions are also designed to learn the multi-level structured semantic representations over the skeleton joints at each frame. Our proposed approach is evaluated on four challenging datasets. The extensive experiments demonstrate the effectiveness of our method for skeleton-based online action prediction.
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