ANALYTICAL AND BOUNDARY ELEMENT SOLUTIONS OF BULK REACTING LINED DUCTS AND PARALLEL-BAFFLE SILENCERS

Lined silencers of various configurations are used to attenuate the noise from building HVAC equipment, gas turbines, and other machinery. First-mode analytical solutions are presented for sound attenuation along rectangular lined ducts, parallel-baffle silencers, and circular lined ducts. The sound absorptive lining is treated using a bulk property model. The analytical solutions entail solving a nonlinear characteristic equation in the transverse direction after the rigid-wall boundary condition is applied. The solution is compared to the boundary element solution and a local impedance analytical solution for several test cases

Research Progress in Genetic Control of Reproductive Performance in Chicken by High-Throughput Sequencing Technology

In chicken, egg production performance is a key trait to the production performance of chickens. Currently, low egg production performance is the major bottleneck, which restraints the development of indigenous chicken industry and blocks the cultivation of new chicken breeds. It has always been the focus of animal genetic breeding in detecting and studying the formation mechanism of complex traits. Due to the egg production is a complex trait determined by multiple genes, and regulated by heredity, environment, and the interaction between them, the mechanism regulating egg-laying performance is yet unknown due to its complexity. With the recent progresses of omics techniques, related researches on it have achieved considerable progress, making it possible to elucidate the molecular mechanism of egg-laying trait now. This article will provide an overall review about the recent research progress in genetic regulation of egg-laying performance in poultry through high-throughput sequencing technology

Understanding and Predicting Delay in Reciprocal Relations

Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.Comment: 10 page

Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page