Diverse foraging strategies of breeding Swinhoe's Storm-petrel in the productive marginal sea of the Northwest Pacific

Understanding the foraging behavior is essential for investigating seabird ecology and conservation,as well as monitoring the well-being of the marine environment.Breeding seabirds adopt diverse foraging strategies to maximize energy gains and cope with the intensified challenges of parenting and self-maintenance.Such tradeoff may stem from the heterogeneity of food resources and the constraints of central place foraging.Nevertheless,abundant marine productivity could alleviate the energy limitation for seabirds,resulting in a consistent foraging approach.Here,we investigated the foraging strategy during the breeding season of a cryptic small-sized seabird,Swinhoe’s Storm-petrel(Hydrobates monorhis),in the Yellow Sea,a productive marginal sea of the Northwest Pacific.Using GPS tracking,we evaluated habitat preference,quantified the foraging strategy,and tested if environmental conditions and individual traits influence foraging trips.We found that Swinhoe’s Storm-petrels preferred nearshore areas with shallow water and engaged in primarily short foraging trips.Distinctive southeastward and southwestward strategies emerged when combining trip metrics,including foraging direction,duration,and maximum distance.The bathymetry,proximity to the coastline,and sea surface temperature differed in two foraging strategies.Foraging strategies exhibited flexibility between individuals,potentially explained by wing morphology,in which longer-winged birds are more likely to embark on longer-distance foraging trips.These findings highlight the impact of environmental factors and individual traits on seabirds’foraging decisions in productive marginal sea ecosystems.Our study also provides valuable insights into the foraging ecology of this Asian endemic storm-petrel.

CREATION OF OPTIMAL MOVEMENT STRATEGY OF PLURAL MOVING OB-JECTS BY GA

The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.

Hydrodynamic modeling strategy for dense to dilute gas-solid fluidized beds

When investigating the hydrodynamic behavior of gas–solid flow systems, there are several options for the drag function, viscosity model, and other parameters. The low accuracy obtained with a random trial and error modeling strategy has led researchers to develop new drag models that are fine-tuned for their specific studies. However, besides the drag functions, an appropriate viscosity model together with radial distribution function have a great impact on the hydrodynamic modeling of fluidized beds. In this study, a detailed validation and verification task is conducted using three different experimental datasets to derive a modeling strategy for predicting hydrodynamic behavior in dense to dilute flow regimes of various fluidized beds. For this purpose, the steady-state Reynolds-averaged Navier–Stokes equations are solved in a finite volume scheme using the twoPhaseEulerFoam solver in the OpenFOAM 2.1.1 software. A comparative study of different drag and viscosity models enables an optimal modeling strategy to be determined for the accurate prediction of the bed pressure drop, bed expansion ratio, time-averaged solid hold-up, and bed height in various dense and dilute flow regimes. Our results show that the modeling strategy prescribed in this study is widely applicable for identifying the hydrodynamic characteristics of various gas–solid fluidized beds with different operating conditions.