Explore the vast range of titles available.

Four kinds of glued laminated timber were produced (i.e., one with a glued edge-joint and the other three with nonglued edge joints) in the lamina at different positions toward the depth direction. Shear tests using an asymmetric four-point bending method were then conducted for these glued laminated timber specimens. The results showed that although the glued edge-joint specimens had the highest shear strength in all groups, the shear strength decreased as the distance from the adjacent nonglued edge-joint plane decreased. Furthermore, the shear strength of all specimens exceeded the standard shear design strength value (2.1 N/mm 2 ) set by the Ministry of Land, Infrastructure, Transport and Tourism, Japan. Next, the shear strength of the nonglued edge-joint specimens was estimated based on that of the glued edge-joint specimens. Although the mean-estimated shear strength was lower than the mean-measured shear strength, the possibility of the shear strength changing based on the position of the nonglued edge-joint plane specimens from that of the glued edge-joint specimens was still estimated.

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. Our motivation comes from the hypothesis that such distinct targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. To this end, we propose a novel fully convolutional neural network using diverse deep supervision within a multi-task framework where bottom layers aim at generating category-agnostic edges, while top layers are responsible for the detection of category-aware semantic edges. To overcome the hypothesized supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated on SBD and Cityscapes datasets.

The$d$-process generates a graph at random by starting with an empty graph with$n$vertices, then adding edges one at a time uniformly at random among all pairs of vertices which have degrees at most$d-1$and are not mutually joined. We show that, in the evolution of a random graph with$n$vertices under the$d$-process with$d$fixed, with high probability, for each$j \\in \\{0,1,\\dots,d-2\\}$, the minimum degree jumps from$j$to$j+1$when the number of steps left is on the order of$\\ln (n)^{d-j-1}$. This answers a question of Ruciński and Wormald. More specifically, we show that, when the last vertex of degree$j$disappears, the number of steps left divided by$\\ln (n)^{d-j-1}$converges in distribution to the exponential random variable of mean$\\frac{j!}{2(d-1)!}$; furthermore, these$d-1$distributions are independent.

Let G = ( V, E ) be a simple finite connected and undirected graph with n vertices and m edges. The n vertices are assigned the colors through mapping c : V [ G ] → I + . An r -dynamic coloring is a proper k -coloring of a graph G such that each vertex of G receive colors in at least min deg ( υ ), r different color classes. The minimum k such that the graph G has r -dynamic k coloring is called the r -dynamic chromatic number of graph G denoted as χ r ( G ). Let G 1 and G 2 be a graphs with n 1 and n 2 vertices and m 1 and m 2 edges. The central vertex join of G 1 and G 2 is the graph G 1   V ˙   G 2 is obtained from C(G 1 ) and G 2 joining each vertex of G 1 with every vertex of G 2 . The aim of this paper is to obtain the lower bound for r -dynamic chromatic number of central vertex join of path with a graph G , central vertex join of cycle with a graph G and r -dynamic chromatic number of P m V ˙   P n ,   P m V ˙   K n ,   P m V ˙   K n ,   P m V ˙   C n ,   C m V ˙   K n and C m V ˙   C n respectively.

Replicated network data are increasingly available in many research fields. For example, in connectomic applications, interconnections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for inference on the entire probability mass function of a network-valued random variable and therefore lack flexibility in characterizing the distribution of relevant topological structures. We propose a flexible Bayesian nonparametric approach for modeling the population distribution of network-valued data. The joint distribution of the edges is defined via a mixture model that reduces dimensionality and efficiently incorporates network information within each mixture component by leveraging latent space representations. The formulation leads to an efficient Gibbs sampler and provides simple and coherent strategies for inference and goodness-of-fit assessments. We provide theoretical results on the flexibility of our model and illustrate improved performance-compared to state-of-the-art models-in simulations and application to human brain networks. Supplementary materials for this article are available online.

Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. In literature, several methods have been proposed to recognize hand gestures with skeletons. One problem with these methods is that they consider little the connectivity between the joints of a skeleton, constructing simple graphs for skeleton connectivity. Observing this, we built a new model of hand skeletons by adding three types of edges in the graph to finely describe the linkage action of joints. Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn more semantic features. Furthermore, relative coordinates are employed to help hand gesture graph convolutional network learn the feature representation independent of the random starting positions of actions. The proposed method is validated on two challenging datasets, and the experimental results show that it outperforms the state-of-the-art methods. Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition.

The extended use of laser welding in the industry requires a less sensitive process in terms of geometrical tolerances of the joint edges. As the industrial availability of laser systems increases, the demand to use laser welding technology possibly with parts coming from less precise production steps is increasing. Gap formation is often caused by the edge quality of the parts coming from previous manufacturing steps such as sheet forming. Al alloy sheets deformed to box-shaped 3D forms often require welded joints on the edges in lap, but, and corner joint configurations. These joints are hard to carry out by laser welding due to the large gap formation caused by the tolerances of the deformation processes involved. Laser welding of Al alloys is already challenging in the absence of gap formation, while these joint configurations have been not feasible with a stationary beam due to incomplete fusion and defect formation. Laser welding with beam oscillation and wire feeding can improve the weldability of these joints. The oscillating motion of the high-intensity beam can achieve a deep weld together with a wider seam. Combined with wire feeding, the process can close gaps in the butt, lap, and corner joint configurations. On the other hand, the added oscillation and wire-related parameters require extending the experimental space, which requires a methodological study to identify feasible conditions. Accordingly, this work proposes a methodological approach to identify and set laser welding process parameters with beam oscillation and wire feeding for an EN AW 5083. Process parameters were initially studied using a simple analytical model that depicts the beam trajectory. Bead-on-plate tests were conducted to assess beam size, power, and weld speed ranges. Lap, butt, and corner joint conditions with a 0.5-mm gap were welded with high quality by manipulating the laser power, oscillation amplitude, and wire feed rate. The results show that welding speeds could be maintained as high as 55 mm/s with complete filling of gaps of up to 0.5 mm, eliminating the surface undercuts and achieving weld widths in the order of 2.5 mm. Moreover the results show the possibility control the depth of the welds from 3 mm to full-penetration conditions.

1. A framework for the description and analysis of multilayer networks is established in statistical physics, and calls are increasing for their adoption by community ecologists. Multilayer networks in community ecology will allow space, time and multiple interaction types to be incorporated into species interaction networks.2. While the multilayer network framework is applicable to ecological questions, it is one thing to be able to describe ecological communities as multilayer networks and another for multilayer networks to actually prove useful for answering ecological questions. Importantly, documenting multilayer network structure requires substantially greater empirical investment than standard ecological networks. In response, we argue that this additional effort is worthwhile and describe a series of research lines where we expect multilayer networks will generate the greatest impact.3. Inter‐layer edges are the key component that differentiate multilayer networks from standard ecological networks. Inter‐layer edges join different networks—termed layers—together and represent ecological processes central to the species interactions studied (e.g., inter‐layer edges representing movement for networks separated in space). Inter‐layer edges may take a variety of forms, be species‐ or network‐specific, and be measured with a large suite of empirical techniques. Additionally, the sheer size of ecological multilayer networks also requires somechanges to empirical data collection around interaction quantification, collaborative efforts and collation in public databases.4. Network ecology has already touched on a wide swath of ecology and evolutionary biology. Because network stability and patterns of species linkage are the most developed areas of network ecology, they are a natural starting place for multilayer investigations. However, multilayer etworks will also provide novel insights to niche partitioning, the connection between traits and species’ interactions, and even the geographic mosaic of co‐evolution.5. Synthesis. Multilayer networks provide a formal way to bring together the study of species interaction networks and the processes that influence them. However, describing inter‐layer edges and the increasing amounts of data required represent challenges. The pay‐off for added investment will be ecological networks that describe the composition and capture the dynamics of ecological communities more completely and, consequently, have greater power for understanding the patterns and processes that underpin diversity in ecological communities.

Graph convolutional networks (GCNs) can well-preserve the structure information of the human body. They have achieved outstanding performance in skeleton-based action recognition. Nevertheless, there are still some issues with existing GCN-based methods. First, all channels have the same adjacency matrix. However, the correlations between joints are complex and may drastically change depending on the actions. These correlations are difficult to fit by merely channel-shared adjacency matrices. Second, the interframe edges of graphs only connect the same joints, neglecting the dependencies between the different joints. Fortunately, convolutional neural networks (CNNs) can simultaneously establish the interdependence of all the points in a spatial-temporal patch. Furthermore, CNNs use different kernels among channels. They are more adaptable for modeling complicated dependencies. In this work, we design a hybrid network (HybridNet) to integrate GCNs and CNNs. The HybridNet not only utilizes structural information well but also models complicated relationships between interframe joints properly.Extensive experiments are conducted on three challenging datasets: NTU-RGB+D, NTU-RGB+D 120, and Skeleton-Kinetics. The proposed model achieves state-of-the-art performance on all these datasets by a considerable margin, demonstrating the superiority of our method. The source code is available at https://github.com/kraus-yang/HybridNet.

Human activities degrade and fragment coastal marine habitats, reducing their structural complexity and making habitat edges a prevalent seascape feature. Though habitat edges frequently are implicated in reduced faunal survival and biodiversity, results of experiments on edge effects have been inconsistent, calling for a mechanistic approach to the study of edges that explicitly includes indirect and interactive effects of habitat alteration at multiple scales across biogeographic gradients. We used an experimental network spanning 17 eelgrass (Zostera marina) sites across the Atlantic and Pacific oceans and the Mediterranean Sea to determine (1) if eelgrass edges consistently increase faunal predation risk, (2) whether edge effects on predation risk are altered by habitat degradation (shoot thinning), and (3) whether variation in the strength of edge effects among sites can be explained by biogeographical variability in covarying eelgrass habitat features. Contrary to expectations, at most sites, predation risk for tethered crustaceans (crabs or shrimps) was lower along patch edges than in patch interiors, regardless of the extent of habitat degradation. However, the extent to which edges reduced predation risk, compared to the patch interior, was correlated with the extent to which edges supported higher eelgrass structural complexity and prey biomass compared to patch interiors. This suggests an indirect component to edge effects in which the impact of edge proximity on predation risk is mediated by the effect of edges on other key biotic factors. Our results suggest that studies on edge effects should consider structural characteristics of patch edges, which may vary geographically, and multiple ways that humans degrade habitats.