A j,υ model for the analysis and prediction of tides

In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwright's calculated results of the tidal potential are used, and the quadratic analysis is made. It has been proved by a number of trials that the harmonic constants of constituents are more stable and the accuracy of the predicted result reliable.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals

Additive manufacturing(AM)has emerged as an advanced technique for the fabrication of complex near-net shaped and lightweight metallic parts with acceptable mechanical performance.The strength of AM metals has been confirmed comparable or even superior to that of metals manufactured by conventional processes,but the fatigue performance is still a knotty issue that may hinder the substitution of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates.As essential complements to high-cost and time-consuming experimental fatigue tests of AM metals,models for fatigue performance prediction are highly desirable.In this review,different models for predicting the fatigue properties of AM metals are summarized in terms of fatigue life,fatigue limit and fatigue crack growth,with a focus on the incorporation of AM characteristics such as AM defect and processing parameters into the models.For predicting the fatigue life of AM metals,empirical models and theoretical models(including local characteristic model,continuum damage mechanics model and probabilistic method)are presented.In terms of fatigue limit,the introduced models involve the Kitagawa–Takahashi model,the Murakami model,the El-Haddad model,etc.For modeling the fatigue crack growth of AM metals,the summarized methodologies include the Paris equation,the Hartman-Schijve equation,the NASGRO equation,the small-crack growth model,and numerical methods.Most of these models for AM metals are similar to those for conventionally processed materials,but are modified and pay more attention to the AM characteristics.Finally,an outlook for possible directions of the modeling and prediction of fatigue properties of AM metals is provided.

Summary of Environmental Impact Assessment for Mining Seafloor Massive Sulfides in Japan

JOGMEC （Japan Oil, Gas and Metals National Corporation） has conducted exploration and research in Japan＇s EEZ （exclusive economic zone） from fiscal year 2008, under contract by the METI （Ministry of Economy, Trade and Industry）, for the commercialization of SMS （Seafloor Massive Sulfide）. As there is currently no commercial mining precedent of SMS, it is necessary to consider the potential impacts of mining on the surrounding environment, and to promote long term sustainable projects. In particular, due to the existence of specific chemosynthetic ecosystems and unique biological communities around the SMS area, both quantitative evaluations of potential environmental impacts and consequent environmental conservation strategies, are necessary in order to avoid and or minimize the potential detrimental effects to the ecosystem, as much as possible. The environmental research programs consist of baseline surveys, environmental impact modeling, and methodological concepts which will be applied to conserve biodiversity. In this paper, we will primarily provide an overview of the project conducted by JOGMEC during 2008-2012.

MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS USING THE SELF-EXCITING THRESHOLD AUTOREGRESSIVE MODEL

When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.

Understanding and Exploiting Information Spreading and Integrating Technologies

Our daily life leaves an increasing amount of digital traces,footprints that are improving our lives.Data-mining tools,like recommender systems,convert these traces to information for aiding decisions in an ever-increasing number of areas in our lives.The feedback loop from what we do,to the information this produces,to decisions what to do next,will likely be an increasingly important factor in human behavior on all levels from individuals to societies.In this essay,we review some effects of this feedback and discuss how to understand and exploit them beyond mapping them on more well-understood phenomena.We take examples from models of spreading phenomena in social media to argue that analogies can be deceptive,instead we need to fresh approaches to the new types of data,something we exemplify with promising applications in medicine.