Parallelism and Optimization of Numerical Ocean Forecasting Model

According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China(MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation(SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface(MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation(172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.

The coexistence of seven sympatric fulvettas in Ailao Mountains,Ejia Town,Yunnan Province

The coexistence of ecologically similar species sharing sympatric areas is a central issue of community ecology. Niche differentiation is required at least in one dimension to avoid competitive exclusion. From 2012-2014, by adopting the methods of mist-nets and point counts to evaluate spatial niche partitioning and morphological differentiations, we explored the coexistence mechanisms of seven sympatric fulvettas in Ailao Mountains, Ejia town, Yunnan Province, China. The microhabitats of these seven fulvettas were significantly different in elevation, roost site height and vegetation coverage, indicating a spatial niche segregation in different levels. Approximately, 90.30% of the samples were correctly classified by linear discriminant analysis（LDA） with correct rates at 91.20%-100%, except the White-browed fulvetta（Alcippe vinipectus）（65.4%） and the Streak-throated fulvetta（A. cinereiceps）（74.6%）. The seven fulvettas were classified into four guilds based on their specific morphological characters, suggesting that the species in each guild use their unique feeding ways to realize resource partitioning in the overlapped areas. These finding indicate that through multi-dimensional spatial niche segregation and divergence in resource utilizing, the interspecific competition among these seven fulvettas is minimized, whereas, coexistence is promoted.

LBLP:Link-Clustering-Based Approach for Overlapping Community Detection

Recently, complex networks have attracted considerable research attention. Community detection is an important problem in the field of complex networks and is useful in a variety of applications such as information propagation, link prediction, recommendation, and marketing. In this study, we focus on discovering overlapping community structures by using link partitions. We propose a Latent Dirichlet Allocation （LDA）-Based Link Partition （LBLP） method, which can find communities with an adjustable range of overlapping. This method employs the LDA model to detect link partitions, which can calculate the community belonging factor for each link. On the basis of this factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of the proposed solution by using both real-world and synthesized networks. The experimental results demonstrate that the approach can find a meaningful and relevant link community structure.

UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

Accurate watermelon yield estimation is crucial to the agricultural value chain,as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning.The conventional method of watermelon yield estimation relies heavily onmanual labor,which is both time-consuming and labor-intensive.To address this,this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle(UAV)videos for detection and counting of watermelons.This pipeline uses You Only Look Once version 8 s(YOLOv8s)with panorama stitching and overlap partitioning,which facilitates the overall number estimation ofwatermelons in field.The watermelon detection model,based on YOLOv8s and obtained using transfer learning,achieved a detection accuracy of 99.20%,demonstrating its potential for application in yield estimation.The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications comparedwith the video tracking based detection and countingmethod.The counting accuracy reached over 96.61%,proving a promising application for yield estimation.The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.