The Laplacian energy of random graphs

Gutman {\it et al.} introduced the concepts of energy \En(G) and Laplacian energy \EnL(G) for a simple graph $G$, and furthermore, they proposed a conjecture that for every graph $G$, \En(G) is not more than \EnL(G). Unfortunately, the conjecture turns out to be incorrect since Liu {\it et al.} and Stevanovi\'c {\it et al.} constructed counterexamples. However, So {\it et al.} verified the conjecture for bipartite graphs. In the present paper, we obtain, for a random graph, the lower and upper bounds of the Laplacian energy, and show that the conjecture is true for almost all graphs.Comment: 14 page

Preserving A Fragile Cultural Heritage Destination: Decoding and Encoding the MoGao Caves

The cultural heritage tourism industry has been on a constant expansion in China across the past decades. A growing volume of regional municipalities and investors is actively participating in developing and transforming the cultural heritage sites into tourism destinations. While the mass tourism and its continuous escalation are bringing tremendous success to regional economy, it is also significantly challenging the material integrity of fragile cultural heritage destinations. Such an issue is exposed most acutely at the Mogao Caves, an archaeological cave-temple ensemble in northwestern China. This thesis lays out the cultural heritage preservation framework and the growing tourism economy in China, dissects the preservation issues and challenges presence at the Mogao Caves, evaluates current Mogao Caves visitation design and narratives, seeks guidance and possible resolutions via international case studies faced with similar challenges, and eventually proposes a vision and plan of redevelopment that aims at encounter-balancing the diverse challenges by integrating findings discovered in the research process

Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss