An improved algorithm for the vertex cover P3P_3 problem on graphs of bounded treewidth

Given a graph $G=(V,E)$ and a positive integer $t\geq2$, the task in thevertex cover $P_t$ ($VCP_t$) problem is to find a minimum subset of vertices$F\subseteq V$ such that every path of order $t$ in $G$ contains at least onevertex from $F$. The $VCP_t$ problem is NP-complete for any integer $t\geq2$and has many applications in real world. Recently, the authors presented adynamic programming algorithm running in time $4^p\cdot n^{O(1)}$ for the$VCP_3$ problem on $n$-vertex graphs with treewidth $p$. In this paper, wepropose an improvement of it and improved the time-complexity to 3^p\cdotn^{O(1)}. The connected vertex cover $P_3$ ($CVCP_3$) problem is the connectedvariation of the $VCP_3$ problem where $G[F]$ is required to be connected.Using the Cut\&Count technique, we give a randomized algorithm with runtime$4^p\cdot n^{O(1)}$ for the $CVCP_3$ problem on $n$-vertex graphs withtreewidth $p$.Comment: arXiv admin note: text overlap with arXiv:1103.0534 by other author

An improved algorithm for the vertex cover P3P_3 problem on graphs of bounded treewidth

Given a graph $G=(V,E)$ and a positive integer $t\geq2$, the task in the vertex cover $P_t$ ($VCP_t$) problem is to find a minimum subset of vertices $F\subseteq V$ such that every path of order $t$ in $G$ contains at least one vertex from $F$. The $VCP_t$ problem is NP-complete for any integer $t\geq2$ and has many applications in real world. Recently, the authors presented a dynamic programming algorithm running in time $4^p\cdot n^{O(1)}$ for the $VCP_3$ problem on $n$-vertex graphs with treewidth $p$. In this paper, we propose an improvement of it and improved the time-complexity to $3^p\cdot n^{O(1)}$. The connected vertex cover $P_3$ ($CVCP_3$) problem is the connected variation of the $VCP_3$ problem where $G[F]$ is required to be connected. Using the Cut\&Count technique, we give a randomized algorithm with runtime $4^p\cdot n^{O(1)}$ for the $CVCP_3$ problem on $n$-vertex graphs with treewidth $p$

TiO2 nanoparticles on nitrogen-doped graphene as anode material for lithium ion batteries

Anatase TiO2 nanoparticles in situ grown on nitrogen-doped, reduced graphene oxide (rGO) have been successfully synthesized as an anode material for the lithium ion battery. The nanosized TiO2 particles were homogeneously distributed on the reduced graphene oxide to inhibit the restacking of the neighbouring graphene sheets. The obtained TiO2/ N-rGO composite exhibits improved cycling performance and rate capability, indicating the important role of reduced graphene oxide, which not only facilitates the formation of uniformly distributed TiO2 nanocrystals, but also increases the electrical conductivity of the composite material. The introduction of nitrogen on the reduced graphene oxide has been proved to increase the conductivity of the reduced graphene oxide and leads to more defects. A disordered structure is thus formed to accommodate more lithium ions, thereby further improving the electrochemical performance

Multicore Parallelized Spatial Overlay Analysis Algorithm Using Vector Polygon Shape Complexity Index Optimization

As core algorithms of geographic computing, overlay analysis algorithms typically have computation-intensive and data-intensive characteristics. It is highly important to optimize overlay analysis algorithms by parallelizing the vector polygons after reasonable data division. To address the problem of unbalanced data partitioning in the task decomposition process for parallel polygon overlay analysis and calculation, this paper presents a data partitioning method based on shape complexity index optimization, which achieves data equalization among multicore parallel computing tasks. Taking the intersection operator and difference operator of the Vatti algorithm as examples, six polygon shape indexes are selected to construct the shape complexity model, and the vector data are divided in accordance with the calculated shape complexity results. Finally, multicore parallelism is achieved based on OpenMP. The experimental results show that when a data set with a large amount of data is used, the effect of the multicore parallel execution of the Vatti algorithm’s intersection operator and difference operator based on shape complexity division is clearly improved. With 16 threads, compared with the serial algorithm, speedups of 29 times and 32 times can be obtained. Compared with the traditional multicore parallel algorithm based on polygon number division, the speed can be improved by 33% and 29%, and the load balancing index is reduced. For a data set with a small amount of data, the acceleration effect of this method is similar to that of traditional methods involving multicore parallelism

Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case

Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images

A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset

Membrane structure-dependent limiting flux behavior and membrane selectivity loss during gypsum scaling : implications for pressure-retarded osmosis operation and membrane design

Herein, we systematically investigated the influence of membrane structural properties on limiting flux behavior and selectivity loss during gypsum scaling in osmotically driven membrane processes. We selected two typical osmotic membranes, thin-film composite (TFC) polyamide (PA) membrane and integrally asymmetric cellulose triacetate (CTA) membrane with different structures, for gypsum scaling tests in active-layer-facing-draw-solution orientation (an operating mode preferred for pressure-retarded osmosis). Compared to the CTA membrane, the TFC membrane suffered severer internal scaling and achieved a lower limiting flux primarily due to its greater structural parameter that induced severer internal concentration polarization (ICP)-enhanced scaling. The limiting flux is inversely proportional to the membrane structural parameter. For the first time we observed that the TFC membrane suffered a drastic loss of integrity and selectivity after gypsum scaling in PRO. We confirmed that the thin PA layer of TFC membrane is more prone to being damaged by the growth of gypsum crystals inside the confined and unstirred support layer, whereas the integrally asymmetric membrane with a thicker active layer could better maintain its integrity. While TFC membrane is the mainstream for PRO in osmotic power harvesting, our study suggests that the integrally asymmetric membrane may be more suitable under severe scaling conditions.Nanyang Technological UniversityThis research was supported by the Faculty of Engineering and Information Technologies Early Career Researcher Funding Scheme at The University of Sydney, Australia. Q.S. is also grateful to the support of the Start-up Grant (SUG) from Nanyang Technological University, Singapore

Cyclic performance of waste-derived SiO2 stabilized, CaO-based sorbents for fast CO2 capture

Calcium-looping technology has been identified as one of the most favorable CO capture techniques for the implementation of carbon capture, utilization, and storage (CCUS); however, the rapid deactivation of CaO sorbents due to sintering is currently a major barrier of this technology. We report for the first time an environmentally benign and cost-effective strategy to reduce sintering by adding waste-derived nanosilica, synthesized from photovoltaic waste (SiCl), into Cao-based sorbents through a simple dry mixing procedure. The as-synthesized sorbent (90% CaCO-W) resulted in final CO uptake of 0.32 g(CO) g(CaO) within 5 min of carbonation. Even under the most severe calcination conditions (at 920 °C in pure CO), it still maintained a stable capture capacity, with CO uptake of 0.23 g(CO) g(CaO) after 30 cycles. Additionally, the CO uptake percentage reached ∼90% in the fast carbonation stage (∼20 s), which is of great significance for real applications. The most likely stabilization mechanism was considered on the basis of N physisorption isotherms and X-ray diffraction patterns. It was concluded that stable and refractory larnite (CaSiO) particles were formed during 2-h thermal pretreatment at 900 °C, leading to sintering resistance. This strategy significantly enhanced the cyclic stability and carbonation rate of CaO-based sorbents through the reuse of SiCl and is thus a green technology for scaled-up fast CO capture

Interstitial Lung Disease in Connective Tissue Disease: A Common Lesion With Heterogeneous Mechanisms and Treatment Considerations

Connective tissue disease (CTD) related interstitial lung disease (CTD-ILD) is one of the leading causes of morbidity and mortality of CTD. Clinically, CTD-ILD is highly heterogenous and involves rheumatic immunity and multiple manifestations of respiratory complications affecting the airways, vessels, lung parenchyma, pleura, and respiratory muscles. The major pathological features of CTD are chronic inflammation of blood vessels and connective tissues, which can affect any organ leading to multi-system damage. The human lung is particularly vulnerable to such damage because anatomically it is abundant with collagen and blood vessels. The complex etiology of CTD-ILD includes genetic risks, epigenetic changes, and dysregulated immunity, which interact leading to disease under various ill-defined environmental triggers. CTD-ILD exhibits a broad spectra of clinical manifestations: from asymptomatic to severe dyspnea; from single-organ respiratory system involvement to multi-organ involvement. The disease course is also featured by remissions and relapses. It can range from stability or slow progression over several years to rapid deterioration. It can also present clinically as highly progressive from the initial onset of disease. Currently, the diagnosis of CTD-ILD is primarily based on distinct pathology subtype(s), imaging, as well as related CTD and autoantibodies profiles. Meticulous comprehensive clinical and laboratory assessment to improve the diagnostic process and management strategies are much needed. In this review, we focus on examining the pathogenesis of CTD-ILD with respect to genetics, environmental factors, and immunological factors. We also discuss the current state of knowledge and elaborate on the clinical characteristics of CTD-ILD, distinct pathohistological subtypes, imaging features, and related autoantibodies. Furthermore, we comment on the identification of high-risk patients and address how to stratify patients for precision medicine management approaches

Confinement Impact for the Dynamics of Supported Metal Nanocatalyst

Supported metal nanoparticles play key roles in nanoelectronics, sensors, energy storage/conversion, and catalysts for the sustainable production of fuels and chemicals. Direct observation of the dynamic processes of nanocatalysts at high temperatures and the confinement of supports is of great significance to investigate nanoparticle structure and functions for practical utilization. Here, in situ high‐resolution transmission electron microscopy photos and videos are combined with dynamics simulations to reveal the real‐time dynamic behavior of Pt nanocatalysts at operation temperatures. Amorphous Pt surface on moving and deforming particles is the working structure during the high operation temperature rather than a static crystal surface and immobilization on supports as proposed before. The free rearrangement of the shape of Pt nanoparticles allows them to pass through narrow windows, which is generally considered to immobilize the particles. The Pt particles, no matter what their sizes, prefer to stay inside nanopores even when they are fast moving near an opening at temperatures up to 900 °C. The porous confinement also blocks the sintering of the particles under the confinement size of pores. These contribute to the continuous high activity and stability of Pt nanocatalysts inside nanoporous supports during a long‐term evaluation of catalytic reforming reaction