Extremal results on the Mostar index of trees with fixed parameters

For a graph $G$, the Mostar index of $G$ is the sum of $|n_u(e)$ - $n_v(e)|$ over all edges $e=uv$ of $G$, where $n_u(e)$ denotes the number of vertices of $G$ that have a smaller distance in $G$ to $u$ than to $v$, and analogously for $n_v(e)$. We determine all the graphs that maximize and minimize the Mostar index respectively over all trees in terms of some fixed parameters like the number of odd vertices, the number of vertices of degree two, and the number of pendent paths of fixed length

Solution to an open problem on the closeness of graphs

A network can be analyzed by means of many graph theoretical parameters. In the context of networks analysis, closeness is a structural metric that evaluates a node's significance inside a network. A cactus is a connected graph in which any block is either a cut edge or a cycle. This paper analyzes the closeness of cacti, we determine the unique graph that minimizes the closeness over all cacti with fixed numbers of vertices and cycles, which solves an open problem proposed by Poklukar \& \v{Z}erovnik [Fundam. Inform. 167 (2019) 219--234]

Large-gap quantum spin Hall insulators in tin films

The search of large-gap quantum spin Hall (QSH) insulators and effective approaches to tune QSH states is important for both fundamental and practical interests. Based on first-principles calculations we find two-dimensional tin films are QSH insulators with sizable bulk gaps of 0.3 eV, sufficiently large for practical applications at room temperature. These QSH states can be effectively tuned by chemical functionalization and by external strain. The mechanism for the QSH effect in this system is band inversion at the \Gamma point, similar to the case of HgTe quantum well. With surface doping of magnetic elements, the quantum anomalous Hall effect could also be realized

In situ epicatechin-loaded hydrogel implants for local drug delivery to spinal column for effective management of post-traumatic spinal injuries

Purpose: To prepare hydrogels loaded with epicatechin, a strong antioxidant,  anti-inflammatory, and neuroprotective tea flavonoid, and characterise them in situ as a vehicle for prolonged and safer drug delivery in patients with post-traumatic spinal cord injury.Methods: Five in situ gel formulations were prepared using chitosan and evaluated in terms of their visual appearance, clarity, pH, viscosity, and in vitro drug release. In vivo anti-inflammatory activity was determined and compared with 2 % piroxicam gel as standard. Motor function activity in a rat model of spinal injury was examined comparatively with i.v. methylprednisolone as standard.Results: The N-methyl pyrrolidone solution (containing 1 % w/w epicatechin with 2 to 10 % w/w chitosan) of the in situ gel formulation had a uniform pH in the range of 4.01 ± 0.12 to 4.27 ± 0.02. High and uniform drug loading, ranging from 94.48 ± 1.28 to 98.08 ± 1.24 %, and good in vitro drug release (79.48 ± 2.84 to 96.48 ± 1.02 % after 7 days) were achieved. The in situ gel prepared from 1 % epicatechin and 2 % chitosan (E5) showed the greatest in vivo anti-inflammatory activity  (60.58 % inhibition of paw oedema in standard carrageenan-induced hind rat paw oedema model, compared with 48.08 % for the standard). The gels showed  significant therapeutic effectiveness against post-traumainduced spinal injury in rats. E5 elicited maximum motor activity (horizontal bar test) in the spinal injuryrat model; the rats that received E5 treatment produced an activity score of 3.62 ± 0.02 at the end of 7 days, compared with 5.0 ± 0.20 following treatment with the standard.Conclusion: In situ epicatechin-loaded gel exhibits significant neuroprotective and anti-inflammatory effects, and therefore can potentially be used for prolonged and safe drug delivery in patients with traumatic spinal cord injury.Keywords: Epicatechin, In situ gel, Chitosan, Spinal injury, Post-traumatic, Motor activity, Antiinflammator

Vision-based pavement marking detection and condition assessment : a case study

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management

PV3D: A 3D Generative Model for Portrait Video Generation

Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos. In this work, we propose PV3D, the first generative framework that can synthesize multi-view consistent portrait videos. Specifically, our method extends the recent static 3D-aware image GAN to the video domain by generalizing the 3D implicit neural representation to model the spatio-temporal space. To introduce motion dynamics to the generation process, we develop a motion generator by stacking multiple motion layers to generate motion features via modulated convolution. To alleviate motion ambiguities caused by camera/human motions, we propose a simple yet effective camera condition strategy for PV3D, enabling both temporal and multi-view consistent video generation. Moreover, PV3D introduces two discriminators for regularizing the spatial and temporal domains to ensure the plausibility of the generated portrait videos. These elaborated designs enable PV3D to generate 3D-aware motion-plausible portrait videos with high-quality appearance and geometry, significantly outperforming prior works. As a result, PV3D is able to support many downstream applications such as animating static portraits and view-consistent video motion editing. Code and models will be released at https://showlab.github.io/pv3d